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Rigel N, Li DW, Brüschweiler R. COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites. Anal Chem 2024; 96:701-709. [PMID: 38157361 PMCID: PMC10794995 DOI: 10.1021/acs.analchem.3c03677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/15/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024]
Abstract
Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.
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Affiliation(s)
- Nick Rigel
- Department
of Chemistry and Biochemistry, The Ohio
State University, Columbus, Ohio 43210, United States
| | - Da-Wei Li
- Campus
Chemical Instrument Center,The Ohio State
University, Columbus, Ohio 43210, 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
- Department
of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, United States
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2
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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3
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Chen X, Bertho G, Caradeuc C, Giraud N, Lucas-Torres C. Present and future of pure shift NMR in metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:654-673. [PMID: 37157858 DOI: 10.1002/mrc.5356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/30/2023] [Accepted: 05/04/2023] [Indexed: 05/10/2023]
Abstract
NMR is one of the most powerful techniques for the analysis of biological samples in the field of metabolomics. However, the high complexity of fluids, tissues, or other biological materials taken from living organisms is still a challenge for state-of-the-art pulse sequences, thereby limiting the detection, the identification, and the quantification of metabolites. In this context, the resolution enhancement provided by broadband homonuclear decoupling methods, which allows for simplifying 1 H multiplet patterns into singlets, has placed this so-called pure shift technique as a promising approach to perform metabolic profiling with unparalleled level of detail. In recent years, the many advances achieved in the design of pure shift experiments has paved the way to the analysis of a wide range of biological samples with ultra-high resolution. This review leads the reader from the early days of the main pure shift methods that have been successfully developed over the last decades to address complex samples, to the most recent and promising applications of pure shift NMR to the field of NMR-based metabolomics.
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Affiliation(s)
- Xi Chen
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | - Gildas Bertho
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | - Cédric Caradeuc
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | - Nicolas Giraud
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | - Covadonga Lucas-Torres
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
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Parlindungan E, Jones OAH. Using metabolomics to understand stress responses in Lactic Acid Bacteria and their applications in the food industry. Metabolomics 2023; 19:99. [PMID: 37999908 DOI: 10.1007/s11306-023-02062-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/07/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Lactic Acid Bacteria (LAB) are commonly used as starter cultures, probiotics, to produce lactic acid and other useful compounds, and even as natural preservatives. For use in any food product however, LAB need to survive the various stresses they encounter in the environment and during processing. Understanding these mechanisms may enable direction of LAB biochemistry with potential beneficial impact for the food industry. AIM OF REVIEW To give an overview of the use of LAB in the food industry and then generate a deeper biochemical understanding of LAB stress response mechanisms via metabolomics, and methods of screening for robust strains of LAB. KEY SCIENTIFIC CONCEPTS OF REVIEW Uses of LAB in food products were assessed and factors which contribute to survival and tolerance in LAB investigated. Changes in the metabolic profiles of LAB exposed to stress were found to be associated with carbohydrates, amino acids and fatty acid levels and these changes were proposed to be a result of the bacteria trying to maintain cellular homeostasis in response to external conditions and minimise cellular damage from reactive oxygen species. This correlates with morphological analysis which shows that LAB can undergo cell elongation and shortening, as well as thinning and thickening of cell membranes, when exposed to stress. It is proposed that these innate strategies can be utilised to minimise negative effects caused by stress through selection of intrinsically robust strains, genetic modification and/or prior exposure to sublethal stress. This work demonstrates the utility of metabolomics to the food industry.
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Affiliation(s)
- Elvina Parlindungan
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research, 31 Biopolis Way, Singapore, 138669, Singapore
| | - Oliver A H Jones
- School of Science, Australian Centre for Research On Separation Science (ACROSS), RMIT University, PO Box 71, Bundoora, VIC, 3083, Australia.
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Shastry A, Dunham-Snary K. Metabolomics and mitochondrial dysfunction in cardiometabolic disease. Life Sci 2023; 333:122137. [PMID: 37788764 DOI: 10.1016/j.lfs.2023.122137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/05/2023]
Abstract
Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.
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Affiliation(s)
- Abhishek Shastry
- Department of Medicine, Queen's University, Kingston, ON, Canada
| | - Kimberly Dunham-Snary
- Department of Medicine, Queen's University, Kingston, ON, Canada; Department of Biomedical & Molecular Sciences, Queen's University, Kingston, ON, Canada.
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Zhang J, Sun M, Elmaidomy AH, Youssif KA, Zaki AMM, Hassan Kamal H, Sayed AM, Abdelmohsen UR. Emerging trends and applications of metabolomics in food science and nutrition. Food Funct 2023; 14:9050-9082. [PMID: 37740352 DOI: 10.1039/d3fo01770b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.
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Affiliation(s)
- Jianye Zhang
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, China
| | - Mingna Sun
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, China
| | - Abeer H Elmaidomy
- Department of Pharmacognosy, Faculty of Pharmacy, Beni-Suef University, Beni-Suef 62511, Egypt
| | - Khayrya A Youssif
- Department of Pharmacognosy, Faculty of Pharmacy, El-Saleheya El Gadida University, Cairo, Egypt
| | - Adham M M Zaki
- Faculty of Pharmacy, Minia University, Minia 61519, Egypt
| | - Hossam Hassan Kamal
- Faculty of Pharmacy, Deraya University, 7 Universities Zone, New Minia 61111, Egypt
| | - Ahmed M Sayed
- Department of Pharmacognosy, Faculty of Pharmacy, Nahda University, 62513 Beni-Suef, Egypt.
- Department of Pharmacognosy, Faculty of Pharmacy, Almaaqal University, 61014 Basra, Iraq
| | - Usama Ramadan Abdelmohsen
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia 61519, Egypt.
- Department of Pharmacognosy, Faculty of Pharmacy, Deraya University, 7 Universities Zone, New Minia 61111, Egypt
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Wenck S, Mix T, Fischer M, Hackl T, Seifert S. Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables. Metabolites 2023; 13:1075. [PMID: 37887402 PMCID: PMC10608983 DOI: 10.3390/metabo13101075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.
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Affiliation(s)
- Soeren Wenck
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
| | - Thorsten Mix
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany;
| | - Markus Fischer
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
| | - Thomas Hackl
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany;
| | - Stephan Seifert
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
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8
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Watermann S, Bode MC, Hackl T. Identification of metabolites from complex mixtures by 3D correlation of 1H NMR, MS and LC data using the SCORE-metabolite-ID approach. Sci Rep 2023; 13:15834. [PMID: 37740032 PMCID: PMC10516956 DOI: 10.1038/s41598-023-43056-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023] Open
Abstract
Not only in metabolomics studies, but also in natural product chemistry, reliable identification of metabolites usually requires laborious steps of isolation and purification and remains a bottleneck in many studies. Direct metabolite identification from a complex mixture without individual isolation is therefore a preferred approach, but due to the large number of metabolites present in natural products, this approach is often hampered by signal overlap in the respective 1H NMR spectra. This paper presents a method for the three-dimensional mathematical correlation of NMR with MS data over the third dimension of the time course of a chromatographic fractionation. The MATLAB application SCORE-metabolite-ID (Semi-automatic COrrelation analysis for REliable metabolite IDentification) provides semi-automatic detection of correlated NMR and MS data, allowing NMR signals to be related to associated mass-to-charge ratios from ESI mass spectra. This approach enables fast and reliable dereplication of known metabolites and facilitates the dynamic analysis for the identification of unknown compounds in any complex mixture. The strategy was validated using an artificial mixture and further tested on a polar extract of a pine nut sample. Straightforward identification of 40 metabolites could be shown, including the identification of β-D-glucopyranosyl-1-N-indole-3-acetyl-N-L-aspartic acid (1) and Nα-(2-hydroxy-2-carboxymethylsuccinyl)-L-arginine (2), the latter being identified in a food sample for the first time.
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Affiliation(s)
- Stephanie Watermann
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146, Hamburg, Germany
| | - Marie-Christin Bode
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146, Hamburg, Germany
| | - Thomas Hackl
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146, Hamburg, Germany.
- Hamburg School of Food Science - Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany.
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Ribay V, Praud C, Letertre MPM, Dumez JN, Giraudeau P. Hyperpolarized NMR metabolomics. Curr Opin Chem Biol 2023; 74:102307. [PMID: 37094508 DOI: 10.1016/j.cbpa.2023.102307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/20/2023] [Accepted: 03/21/2023] [Indexed: 04/26/2023]
Abstract
Hyperpolarized NMR is a promising approach to address the sensitivity limits of conventional NMR metabolomics approaches, which currently fails to detect minute metabolite concentrations in biological samples. This review describes how tremendous signal enhancement offered by dissolution-dynamic nuclear polarization and parahydrogen-based techniques can be fully exploited for molecular omics sciences. Recent developments, including the combination of hyperpolarization techniques with fast multi-dimensional NMR implementation and quantitative workflows are described, and a comprehensive comparison of existing hyperpolarization techniques is proposed. High-throughput, sensitivity, resolution and other relevant challenges that should be tackled for a general application of hyperpolarized NMR in metabolomics are discussed.
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Affiliation(s)
- Victor Ribay
- Nantes Université, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
| | - Clément Praud
- Nantes Université, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
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Dasgupta S, Ghosh N, Bhattacharyya P, Roy Chowdhury S, Chaudhury K. Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview. Crit Rev Clin Lab Sci 2023; 60:153-170. [PMID: 36420874 DOI: 10.1080/10408363.2022.2140329] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.
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Affiliation(s)
- Sanjukta Dasgupta
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Nilanjana Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
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Osman A, Chittiboyina AG, Avula B, Ali Z, Adams SJ, Khan IA. Quality Consistency of Herbal Products: Chemical Evaluation. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2023; 122:163-219. [PMID: 37392312 DOI: 10.1007/978-3-031-26768-0_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2023]
Abstract
The widespread utility of herbal products has been rising considerably worldwide, including both developed and developing countries, leading to the rapid growth of their availability in the United States and globally. This substantial increase in consumption of herbal products has witnessed the emergence of adverse effects upon oral administration of certain of these products, and thus has raised safety concerns. The adverse effects caused by the consumption of certain botanical medicines occur primarily as a result of the poor quality of plant raw materials or the finished products, which inherently may affect safety and/or efficacy. The poor quality of some herbal products can be attributed to a lack of proper quality assurance and quality control. A high demand for herbal products that surpasses production, combined with a desire for maximizing profits, along with a lack of rigorous quality control within some manufacturing facilities have led to the emergence of quality inconsistencies. The underlying causes for this involve the misidentification of plant species, or their substitution, adulteration, or contamination with harmful ingredients. Analytical assessments have revealed there to be frequent and significant compositional variations between marketed herbal products. The inconsistency of the quality of herbal products can be ascribed essentially to the inconsistency of the botanical raw material quality used to manufacture the products. Thus, the quality assurance and the quality control of the botanical raw materials is may contribute significantly to improving the quality and consistency of the quality of the end products. The current chapter focuses on the chemical evaluation of quality and consistency of herbal products, including botanical dietary supplements. Different techniques, instruments, applications, and methods used in identifying, quantifying, and generating chemical fingerprints and chemical profiles of the ingredients of the herbal products will be described. The strengths and weaknesses of the various techniques available will be addressed. Limitations of the other approaches including morphological or microscopic analysis and DNA-based analysis will be presented.
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Affiliation(s)
- Ahmed Osman
- School of Pharmacy, National Center for Natural Products Research, The University of Mississippi, University, MS, 38677, USA.
| | - Amar G Chittiboyina
- School of Pharmacy, National Center for Natural Products Research, The University of Mississippi, University, MS, 38677, USA
| | - Bharathi Avula
- School of Pharmacy, National Center for Natural Products Research, The University of Mississippi, University, MS, 38677, USA
| | - Zulfiqar Ali
- School of Pharmacy, National Center for Natural Products Research, The University of Mississippi, University, MS, 38677, USA
| | - Sebastian J Adams
- School of Pharmacy, National Center for Natural Products Research, The University of Mississippi, University, MS, 38677, USA
| | - Ikhlas A Khan
- School of Pharmacy, National Center for Natural Products Research, The University of Mississippi, University, MS, 38677, USA
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12
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Abstract
Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.
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Affiliation(s)
- Zuhal Eraslan
- Department of Dermatology, Weill Cornell Medicine, New York, NY, USA
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of University of Barcelona (IBUB), University of Barcelona, Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Ulrich L Günther
- Institute of Chemistry and Metabolomics, University of Lübeck, Lübeck, Germany.
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Kil YS, Baral A, Jeong BS, Laatikainen P, Liu Y, Han AR, Hong MJ, Kim JB, Choi H, Park PH, Nam JW. Combining NMR and MS to Describe Pyrrole-2-Carbaldehydes in Wheat Bran of Radiation. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:13002-13014. [PMID: 36167496 DOI: 10.1021/acs.jafc.2c04771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are indispensable analytical tools to provide chemical fingerprints in metabolomics studies. The present study evaluated radiation breeding wheat lines for chemical changes by non-targeted NMR-based metabolomics analysis of bran extracts. Multivariate analysis following spectral binning suggested pyrrole-2-carbaldehydes as chemical markers of four mutant lines with distinct NMR fingerprints in a δH range of 9.28-9.40 ppm. Further NMR and MS data analysis, along with chromatographic fractionation and synthetic preparation, aimed at structure identification of marker metabolites and identified five pyrrole-2-carbaldehydes. Quantum-mechanical driven 1H iterative full spin analysis (QM-HiFSA) on synthetic pyrrole-2-carbaldehydes provided a precise description of complex peak patterns. Biological evaluation of pyrrole-2-carbaldehydes was performed with nine synthetic products, and six compounds showed hepatoprotective effects via modulation of reactive oxygen species production. Given that three out of five identified in wheat bran of radiation were described for hepatoprotective activity, the value of radiation mutation to greatly enhance pyrrole-2-carbaldehyde production was supported.
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Affiliation(s)
- Yun-Seo Kil
- College of Pharmacy, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
| | - Ananda Baral
- College of Pharmacy, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
| | - Byeong-Seon Jeong
- College of Pharmacy, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
| | | | - Yang Liu
- Product Quality & Analytical Method Department, United States Pharmacopeial Convention, Rockville, Maryland 20852, United States
| | - Ah-Reum Han
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup-si, Jeollabuk-do 56212, South Korea
| | - Min-Jeong Hong
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup-si, Jeollabuk-do 56212, South Korea
| | - Jin-Baek Kim
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup-si, Jeollabuk-do 56212, South Korea
| | - Hyukjae Choi
- College of Pharmacy, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
| | - Pil-Hoon Park
- College of Pharmacy, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
| | - Joo-Won Nam
- College of Pharmacy, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan-si, Gyeongsangbuk-do 38541, South Korea
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14
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Yesiltepe Y, Govind N, Metz TO, Renslow RS. An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts. J Cheminform 2022; 14:64. [PMID: 36138446 PMCID: PMC9499888 DOI: 10.1186/s13321-022-00587-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/06/2022] [Indexed: 11/24/2022] Open
Abstract
The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.
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Affiliation(s)
- Yasemin Yesiltepe
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA.,Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Niranjan Govind
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas O Metz
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA
| | - Ryan S Renslow
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA. .,Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
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15
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Wang K, Yuan Y, Luo X, Shen Z, Huang Y, Zhou H, Gao X. Effects of exogenous selenium application on nutritional quality and metabolomic characteristics of mung bean ( Vigna radiata L.). FRONTIERS IN PLANT SCIENCE 2022; 13:961447. [PMID: 36061759 PMCID: PMC9433778 DOI: 10.3389/fpls.2022.961447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 07/26/2022] [Indexed: 06/15/2023]
Abstract
Selenium (Se) biofortification is an important strategy for reducing hidden hunger by increasing the nutritional quality of crops. However, there is limited metabolomic information on the nutritional quality of Se-enriched mung beans. In this study, physiological assays and LC-MS/MS based widely targeted metabolomics approach was employed to reveal the Se biofortification potential of mung bean by evaluating the effect of Se on mung bean nutraceutical compounds and their qualitative parameters. Physiological data showed that foliar application of 30 g ha-1 Se at key growth stages significantly increased the content of Se, protein, fat, total phenols, and total flavonoids content in two mung bean varieties. Widely targeted metabolomics identified 1,080 metabolites, among which L-Alanyl-L-leucine, 9,10-Dihydroxy-12,13-epoxyoctadecanoic acid, and 1-caffeoylquinic acid could serve as biomarkers for identifying highly nutritious mung bean varieties. Pathway enrichment analysis revealed that the metabolic pathways of different metabolites were different in the Se-enriched mung bean. Specifically, P1 was mainly enriched in the linoleic acid metabolic pathway, while P2 was mainly enriched in the phosphonate and phosphinate metabolic pathways. Overall, these results revealed the specific Se enrichment mechanism of different mung bean varieties. This study provides new insights into the comprehensive improvement of the nutritional quality of mung beans.
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16
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Metabolomics with multi-block modelling of mass spectrometry and nuclear magnetic resonance in order to discriminate Haplosclerida marine sponges. Anal Bioanal Chem 2022; 414:5929-5942. [PMID: 35725831 DOI: 10.1007/s00216-022-04158-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 12/11/2022]
Abstract
A comprehensive metabolomic strategy, integrating 1H NMR and MS-based multi-block modelling in conjunction with multi-informational molecular networking, has been developed to discriminate sponges of the order Haplosclerida, well known for being taxonomically contentious. An in-house collection of 33 marine sponge samples belonging to three families (Callyspongiidae, Chalinidae, Petrosiidae) and four different genera (Callyspongia, Haliclona, Petrosia, Xestospongia) was investigated using LC-MS/MS, molecular networking, and the annotations processes combined with NMR data and multivariate statistical modelling. The combination of MS and NMR data into supervised multivariate models led to the discrimination of, out of the four genera, three groups based on the presence of metabolites, not necessarily previously described in the Haplosclerida order. Although these metabolomic methods have already been applied separately, it is the first time that a multi-block untargeted approach using MS and NMR has been combined with molecular networking and statistically analyzed, pointing out the pros and cons of this strategy.
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17
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Morgan EW, Perdew GH, Patterson AD. Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research. Toxicol Sci 2022; 187:189-213. [PMID: 35285497 PMCID: PMC9154275 DOI: 10.1093/toxsci/kfac029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.
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Affiliation(s)
- Ethan W Morgan
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Gary H Perdew
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Andrew D Patterson
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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18
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Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021; 8:698337. [PMID: 34616770 PMCID: PMC8488110 DOI: 10.3389/fmolb.2021.698337] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
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Affiliation(s)
| | | | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research of Medicine (CIRM), Department of Pharmacy, Université de Liège, Liège, Belgique
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19
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Ivashchenko DA, Cerqueira NM, Magalhães AL. Improving computational modeling coupled with ion mobility-mass spectrometry data for efficient drug metabolite structural determination. Struct Chem 2021. [DOI: 10.1007/s11224-021-01726-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nat Methods 2021; 18:733-746. [PMID: 33972782 DOI: 10.1038/s41592-021-01116-4] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 03/12/2021] [Indexed: 02/03/2023]
Abstract
Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.
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21
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Borges R, Colby SM, Das S, Edison AS, Fiehn O, Kind T, Lee J, Merrill AT, Merz KM, Metz TO, Nunez JR, Tantillo DJ, Wang LP, Wang S, Renslow RS. Quantum Chemistry Calculations for Metabolomics. Chem Rev 2021; 121:5633-5670. [PMID: 33979149 PMCID: PMC8161423 DOI: 10.1021/acs.chemrev.0c00901] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Indexed: 02/07/2023]
Abstract
A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.
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Affiliation(s)
- Ricardo
M. Borges
- Walter
Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Sean M. Colby
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Susanta Das
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Arthur S. Edison
- Departments
of Genetics and Biochemistry and Molecular Biology, Complex Carbohydrate
Research Center and Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
| | - Oliver Fiehn
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Tobias Kind
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Jesi Lee
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Amy T. Merrill
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Thomas O. Metz
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nunez
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Dean J. Tantillo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Shunyang Wang
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ryan S. Renslow
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
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22
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Xiong F, Nie X, Yang L, Wang L, Li J, Zhou G. Non-target metabolomics revealed the differences between Rh. tanguticum plants growing under canopy and open habitats. BMC PLANT BIOLOGY 2021; 21:119. [PMID: 33639841 PMCID: PMC7913229 DOI: 10.1186/s12870-021-02897-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 02/21/2021] [Indexed: 05/28/2023]
Abstract
BACKGROUND Rheum tanguticum (Rh. tanguticum) is an important traditional Chinese medicine plant, "Dahuang", which contains productive metabolites and occupies wide habitats on the Qinghai-Tibet plateau. Plants occupying wide habitats usually vary in phenotypes such as in morphology and metabolism, thereby developing into different ecotypes. Under canopy and open habitats are a pair of dissimilar habitats which possess Rh. tanguticum plants. However, few studies have focused on the effect of habitats on Rh. tanguticum growth, particularly combining morphological and metabolic changes. This study focused on Rh. tanguticum plants growing in under canopy and open habitats where morphology and metabolism changes were quantified using non-target metabolism methods. RESULTS The obtained results indicated that the two dissimilar habitats led to Rh. tanguticum developing into two distinct ecotypes where the morphology and metabolism were simultaneously changed. Under canopy habitats bred morphologically smaller Rh. tanguticum plants which had a higher level of metabolites (22 out of 31) which included five flavonoids, four isoflavonoids, and three anthracenes. On the other hand, the open habitats produced morphologically larger Rh. tanguticum plants having a higher level of metabolites (9 out of 31) including four flavonoids. 6 of the 31 metabolites were predicted to have effect targets, include 4 represent for under canopy habitats and 2 for open habitats. Totally, 208 targets were connected, among which 42 were communal targets for both under canopy and open habitats represent compounds, and 100 and 66 were unique targets for under canopy superior compounds and open habitats superior compounds, respectively. In addition, aloe-emodin, emodin, chrysophanol, physcion, sennoside A and sennoside B were all more accumulated in under canopy habitats, and among which aloe-emodin, emodin, chrysophanol and physcion were significantly higher in under canopy habitats. CONCLUSIONS This study determined that Rh. tanguticum growing in under canopy and in open habitats developed into two distinct ecotypes with morphological and metabolic differences. Results of network pharmacology study has indicated that "Dahuang" coming from different habitats, such as under canopy and open habitats, are different in effect targets and thus may have different medicinal use. According to target metabolomics, under canopy habitats may grow better "Dahuang".
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Affiliation(s)
- Feng Xiong
- CAS Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Xining, 810008, China
- College of Resources and Environment, University of Chinese Academy of Science, Beijing, 100049, China
| | - Xiuqing Nie
- Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry Chinese Academy of Forestry, Beijing, 100091, China
- Research Institute of Nature Protected Area Chinese Academy of Forestry, Beijing, 100091, China
| | - Lucun Yang
- CAS Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Xining, 810008, China
| | - Lingling Wang
- CAS Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Xining, 810008, China
- College of Resources and Environment, University of Chinese Academy of Science, Beijing, 100049, China
| | - Jingjing Li
- College of Life Sciences, Qinghai Normal University, Xining, 810008, China
| | - Guoying Zhou
- CAS Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Xining, 810008, China.
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23
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Nagy T, Róth G, Kuki Á, Zsuga M, Kéki S. Mass Spectral Filtering by Mass-Remainder Analysis (MARA) at High Resolution and Its Application to Metabolite Profiling of Flavonoids. Int J Mol Sci 2021; 22:ijms22020864. [PMID: 33467107 PMCID: PMC7830504 DOI: 10.3390/ijms22020864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/13/2021] [Accepted: 01/14/2021] [Indexed: 11/16/2022] Open
Abstract
Flavonoids represent an important class of secondary metabolites because of their potential health benefits and functions in plants. We propose a novel method for the comprehensive flavonoid filtering and screening based on direct infusion mass spectrometry (DIMS) analysis. The recently invented data mining procedure, the multi-step mass-remainder analysis (M-MARA) technique is applied for the effective mass spectral filtering of the peak rich spectra of natural herb extracts. In addition, our flavonoid-filtering algorithm facilitates the determination of the elemental composition. M-MARA flavonoid-filtering uses simple mathematical and logical operations and thus, it can easily be implemented in a regular spreadsheet software. A huge benefit of our method is the high speed and the low demand for computing power and memory that enables the real time application even for tandem mass spectrometric analysis. Our novel method was applied for the electrospray ionization (ESI) DIMS spectra of various herb extract, and the filtered mass spectral data were subjected to chemometrics analysis using principal component analysis (PCA).
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Affiliation(s)
- Tibor Nagy
- Department of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary; (T.N.); (G.R.); (Á.K.); (M.Z.)
| | - Gergő Róth
- Department of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary; (T.N.); (G.R.); (Á.K.); (M.Z.)
- Doctoral School of Chemistry, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary
| | - Ákos Kuki
- Department of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary; (T.N.); (G.R.); (Á.K.); (M.Z.)
| | - Miklós Zsuga
- Department of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary; (T.N.); (G.R.); (Á.K.); (M.Z.)
| | - Sándor Kéki
- Department of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary; (T.N.); (G.R.); (Á.K.); (M.Z.)
- Correspondence: ; Fax: +36-52-518662
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24
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Raja G, Jung Y, Jung SH, Kim TJ. 1H-NMR-based metabolomics for cancer targeting and metabolic engineering –A review. Process Biochem 2020. [DOI: 10.1016/j.procbio.2020.08.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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25
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Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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26
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Letertre MPM, Dervilly G, Giraudeau P. Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics. Anal Chem 2020; 93:500-518. [PMID: 33155816 DOI: 10.1021/acs.analchem.0c04371] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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27
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Abstract
A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, USA
| | - Arthur S. Edison
- Departments of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, USA
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, USA
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28
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Zhang J, Terayama K, Sumita M, Yoshizoe K, Ito K, Kikuchi J, Tsuda K. NMR-TS: de novo molecule identification from NMR spectra. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2020; 21:552-561. [PMID: 32939179 PMCID: PMC7476483 DOI: 10.1080/14686996.2020.1793382] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/05/2020] [Accepted: 07/05/2020] [Indexed: 05/09/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.
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Affiliation(s)
- Jinzhe Zhang
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Kei Terayama
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
- RIKEN Medical Sciences Innovation Hub Program (MIH), Yokohama, Japan
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Masato Sumita
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Japan
| | - Kazuki Yoshizoe
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Kengo Ito
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Jun Kikuchi
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Koji Tsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Japan
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29
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Li S, Tian Y, Jiang P, Lin Y, Liu X, Yang H. Recent advances in the application of metabolomics for food safety control and food quality analyses. Crit Rev Food Sci Nutr 2020; 61:1448-1469. [PMID: 32441547 DOI: 10.1080/10408398.2020.1761287] [Citation(s) in RCA: 145] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.
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Affiliation(s)
- Shubo Li
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Yufeng Tian
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Pingyingzi Jiang
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Ying Lin
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Xiaoling Liu
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Hongshun Yang
- Department of Food Science & Technology, National University of Singapore, Singapore, Singapore
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Kuhn S, Colreavy-Donnelly S, Santana de Souza J, Borges RM. An integrated approach for mixture analysis using MS and NMR techniques. Faraday Discuss 2020; 218:339-353. [PMID: 31114813 DOI: 10.1039/c8fd00227d] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We suggest an improved software pipeline for mixture analysis. The improvements include combining tandem MS and 2D NMR data for a reliable identification of the constituents in an algorithm based on network analysis aiming for a robust and reliable identification routine. An important part of this pipeline is the use of open-data repositories, although it is not totally reliant on them. The NMR identification step emphasizes robustness and is less sensitive towards changes in data acquisition and processing than existing methods. The process starts with LC-ESI-MSMS based molecular network dereplication using data from the GNPS collaborative collection. We identify closely related structures by propagating structure elucidation through edges in the network. Those identified compounds are added on top of a candidate list for the following NMR filtering method that predicts HSQC and HMBC NMR data. The similarity of the predicted spectra of the set of closely related structures to the measured spectra of the mixture sample is taken as one indication of the most likely candidates for its compounds. The other indication is the match of the spectra to clusters built by a network analysis from the spectra of the mixture. The sensitivity gap between NMR and MS is anticipated and it will be reflected naturally by the eventual identification of fewer compounds, but with a higher confidence level, after the NMR analysis step. The contributions of the paper are an algorithm combining MS and NMR spectroscopy and a robust nJCH network analysis to explore the complementary aspects of both techniques. This delivers good results, even if a perfect computational separation of the compounds in the mixture is not possible. All of the scripts are freely available to aid studies such as with plants, marine organisms, and microorganism natural product chemistry and metabolomics, as those are the driving forces for this project.
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Affiliation(s)
- Stefan Kuhn
- De Montfort University, School of Computer Science and Informatics, The Gateway, Leicester LE1 9BH, UK.
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Morozov SV, Tkacheva NI, Tkachev AV. On Problems of the Comprehensive Chemical Profiling of Medicinal Plants. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2020. [DOI: 10.1134/s1068162019070070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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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: 111] [Impact Index Per Article: 27.8] [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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Abstract
The microbiome residing in anaerobic digesters drives the anaerobic digestion (AD) process to convert various feedstocks to biogas as a renewable source of energy. This microbiome has been investigated in numerous studies in the last century. The early studies used cultivation-based methods and analysis to identify the four guilds (or functional groups) of microorganisms. Molecular biology techniques overcame the limitations of cultivation-based methods and allowed the identification of unculturable microorganisms, revealing the high diversity of microorganisms involved in AD. In the past decade, omics technologies, including metataxonomics, metagenomics, metatranscriptomics, metaproteomics, and metametabolomics, have been or start to be used in comprehensive analysis and studies of biogas-producing microbiomes. In this chapter, we reviewed the utilities and limitations of these analysis methods, techniques, and technologies when they were used in studies of biogas-producing microbiomes, as well as the new information on diversity, composition, metabolism, and syntrophic interactions of biogas-producing microbiomes. We also discussed the current knowledge gaps and the research needed to further improve AD efficiency and stability.
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Giraudeau P. NMR-based metabolomics and fluxomics: developments and future prospects. Analyst 2020; 145:2457-2472. [DOI: 10.1039/d0an00142b] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.
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Wang C, Zhang B, Timári I, Somogyi Á, Li DW, Adcox HE, Gunn JS, Bruschweiler-Li L, Brüschweiler R. Accurate and Efficient Determination of Unknown Metabolites in Metabolomics by NMR-Based Molecular Motif Identification. Anal Chem 2019; 91:15686-15693. [PMID: 31718151 DOI: 10.1021/acs.analchem.9b03849] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Knowledge of the chemical identity of metabolite molecules is critical for the understanding of the complex biological systems to which they belong. Since metabolite identities and their concentrations are often directly linked to the phenotype, such information can be used to map biochemical pathways and understand their role in health and disease. A very large number of metabolites however are still unknown; i.e., their spectroscopic signatures do not match those in existing databases, suggesting unknown molecule identification is both imperative and challenging. Although metabolites are structurally highly diverse, the majority shares a rather limited number of structural motifs, which are defined by sets of 1H and 13C chemical shifts of the same spin system. This allows one to characterize unknown metabolites by a divide-and-conquer strategy that identifies their structural motifs first. Here, we present the structural motif-based approach "SUMMIT Motif" for the de novo identification of unknown molecular structures in complex mixtures, without the need for extensive purification, using NMR in tandem with two newly curated NMR molecular structural motif metabolomics databases (MSMMDBs). For the identification of structural motif(s), first, the 1H and 13C chemical shifts of all the individual spin systems are extracted from 2D and 3D NMR spectra of the complex mixture. Next, the molecular structural motifs are identified by querying these chemical shifts against the new MSMMDBs. One database, COLMAR MSMMDB, was derived from experimental NMR chemical shifts of known metabolites taken from the COLMAR metabolomics database, while the other MSMMDB, pNMR MSMMDB, is based on predicted chemical shifts of metabolites of several existing large metabolomics databases. For molecules consisting of multiple spin systems, spin systems are connected via long-range scalar J-couplings. When this motif-based identification method was applied to the hydrophilic extract of mouse bile fluid, two unknown metabolites could be successfully identified. This approach is both accurate and efficient for the identification of unknown metabolites and hence enables the discovery of new biochemical processes and potential biomarkers.
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Wishart DS. NMR metabolomics: A look ahead. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:155-161. [PMID: 31377153 DOI: 10.1016/j.jmr.2019.07.013] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 04/13/2019] [Accepted: 07/08/2019] [Indexed: 05/24/2023]
Abstract
NMR has been used to perform metabolic studies, metabolic profiling and metabolomics in biofluids and tissues for more than 40 years. This close connection between metabolic measurements and NMR has flourished because of NMR's many unique strengths for characterizing the chemical composition of complex mixtures. However, a number of other technologies, including mass spectrometry, have appeared in the past few years that are encroaching on NMR's dominance in metabolomics and metabolic studies. In this brief review, some of the current strengths and existing limitations of NMR-based metabolomics are highlighted. Additionally, a number of recent advances in NMR hardware, methodology and software are also described and these advancements are used to speculate about where NMR-based metabolomics is going, what needs to be done to make it more popular and how it will evolve in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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Wang XJ, Ren JL, Zhang AH, Sun H, Yan GL, Han Y, Liu L. Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence. MASS SPECTROMETRY REVIEWS 2019; 38:380-402. [PMID: 30817039 DOI: 10.1002/mas.21589] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 01/25/2019] [Indexed: 06/09/2023]
Abstract
Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.
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Affiliation(s)
- Xi-Jun Wang
- National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau
- National Engineering Laboratory for the Development of Southwestern Endangered Medicinal Materials, Guangxi Botanical Garden of Medicinal Plant, Nanning Guangxi, China
| | - Jun-Ling Ren
- National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ai-Hua Zhang
- National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Hui Sun
- National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Guang-Li Yan
- National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ying Han
- National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Liang Liu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau
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Palaric C, Pilard S, Fontaine JX, Boccard J, Mathiron D, Rigaud S, Cailleu D, Mesnard F, Gut Y, Renaud T, Petit A, Beaumal JY, Molinié R. Processing of NMR and MS metabolomics data using chemometrics methods: a global tool for fungi biotransformation reactions monitoring. Metabolomics 2019; 15:107. [PMID: 31346787 DOI: 10.1007/s11306-019-1567-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 07/16/2019] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Biotransformation constitutes an important aspect of the drug discovery process, to mimic human metabolism of active principal ingredient but also to generate new chemical entities. Several microorganisms such as fungi are well adapted to transform drug, whether at the stage of screening or for large-scale production. OBJECTIVES Due to the high chemical complexity of the biotransformation media, it seems attractive to develop new analytical strategies in order to guarantee an adequate monitoring and optimize the production of targeted metabolites or drug candidates. METHODS The model designed for this purpose concerns the biotransformation of a potential histamine H3 antagonist (S38093) in order to produce phase I metabolites. MS, NMR and chemometrics tools were used to monitor biotransformation reactions. RESULTS First, a screening of eleven filamentous fungi was carried out by UHPLC-UV-MS and principal component analysis to select the best candidates. Subsequently, MS (tR, m/z) and NMR (1H, JRES) fingerprints associated with Consensus OPLS-DA multiblock approach were used to better understand the bioreaction mechanisms in terms of nutrient consumption and hydroxylated metabolites production. Then an experimental design was set up to optimize the production conditions (pH, kinetic) of these target metabolites. CONCLUSION This study demonstrates how NMR and MS acquisitions combined with chemometric methods offer an innovative analytical strategy to have a grasp of functionalization mechanisms, and identify metabolites and other compounds (amino acids, nutrients, etc.) in complex biotransformation mixtures.
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Affiliation(s)
- Cécile Palaric
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | - Serge Pilard
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France.
| | - Jean-Xavier Fontaine
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1211, Geneva 4, Switzerland
| | - David Mathiron
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
| | - Sébastien Rigaud
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
| | - Dominique Cailleu
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
| | - François Mesnard
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
| | - Yoann Gut
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | - Tristan Renaud
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | - Alain Petit
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | | | - Roland Molinié
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
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Monge ME, Dodds JN, Baker ES, Edison AS, Fernández FM. Challenges in Identifying the Dark Molecules of Life. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2019; 12:177-199. [PMID: 30883183 PMCID: PMC6716371 DOI: 10.1146/annurev-anchem-061318-114959] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.
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Affiliation(s)
- María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1425FQD, Ciudad de Buenos Aires, Argentina
| | - James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Arthur S Edison
- Department of Genetics, Department of Biochemistry and Molecular Biology, and Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology and Petit Institute for Biochemistry and Bioscience, Atlanta, Georgia 30332, USA;
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Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures. Anal Bioanal Chem 2019; 411:1301-1309. [PMID: 30793214 DOI: 10.1007/s00216-019-01600-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/16/2018] [Accepted: 01/11/2019] [Indexed: 01/02/2023]
Abstract
NMR and LC-MS are two powerful techniques for metabolomics studies. In NMR spectra and LC-MS data collected on a series of metabolite mixtures, signals of the same individual metabolite are quantitatively correlated, based on the fact that NMR and LC-MS signals are derived from the same metabolite covary. Deconvoluting NMR spectra and LC-MS data of the mixtures through this kind of statistical correlation, NMR and LC-MS spectra of individual metabolites can be obtained as if the specific metabolite is virtually isolated from the mixture. Integrating NMR and LC-MS spectra, more abundant and orthogonal information on the same compound can significantly facilitate the identification of individual metabolites in the mixture. This strategy was demonstrated by deconvoluting 1D 13C, DEPT, HSQC, TOCSY, and LC-MS spectra acquired on 10 mixtures consisting of 6 typical metabolites with varying concentration. Based on statistical correlation analysis, NMR and LC-MS signals of individual metabolites in the mixtures can be extracted as if their spectra are acquired on the purified metabolite, which notably facilitates structure identification. Statistically correlating NMR spectra and LC-MS data (CoNaM) may represent a novel approach to identification of individual compounds in a mixture. The success of this strategy on the synthetic metabolite mixtures encourages application of the proposed strategy of CoNaM to biological samples (such as serum and cell extracts) in metabolomics studies to facilitate identification of potential biomarkers.
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Timári I, Wang C, Hansen AL, Costa dos Santos G, Ok Yoon S, Bruschweiler-Li L, Brüschweiler R. Real-Time Pure Shift HSQC NMR for Untargeted Metabolomics. Anal Chem 2019; 91:2304-2311. [PMID: 30608652 PMCID: PMC6386528 DOI: 10.1021/acs.analchem.8b04928] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Sensitivity and resolution are key considerations for NMR applications in general and for metabolomics in particular, where complex mixtures containing hundreds of metabolites over a large range of concentrations are commonly encountered. There is a strong demand for advanced methods that can provide maximal information in the shortest possible time frame. Here, we present the optimization and application of the recently introduced 2D real-time BIRD 1H-13C HSQC experiment for NMR-based metabolomics of aqueous samples at 13C natural abundance. For mouse urine samples, it is demonstrated how this real-time pure shift sensitivity-improved heteronuclear single quantum correlation method provides broadband homonuclear decoupling along the proton detection dimension and thereby significantly improves spectral resolution in regions that are affected by spectral overlap. Moreover, the collapse of the scalar multiplet structure of cross-peaks leads to a sensitivity gain of about 40-50% over a traditional 2D HSQC-SI experiment. The experiment works well over a range of magnetic field strengths and is particularly useful when resonance overlap in crowded regions of the HSQC spectra hampers accurate metabolite identification and quantitation.
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Affiliation(s)
- István Timári
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Cheng Wang
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Alexandar L. Hansen
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Gilson Costa dos Santos
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Sung Ok Yoon
- Department of Biological Chemistry and Pharmacology, 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
| | - 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
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, United States
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. High-Throughput Metabolomics by 1D NMR. Angew Chem Int Ed Engl 2019; 58:968-994. [PMID: 29999221 PMCID: PMC6391965 DOI: 10.1002/anie.201804736] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Indexed: 12/12/2022]
Abstract
Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.
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Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P.Via Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Veronica Ghini
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Gaia Meoni
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Cristina Licari
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of FlorenceLargo Brambilla 3FlorenceItaly
| | - Paola Turano
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
| | - Claudio Luchinat
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
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Leggett A, Wang C, Li DW, Somogyi A, Bruschweiler-Li L, Brüschweiler R. Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics. Methods Enzymol 2019; 615:407-422. [PMID: 30638535 PMCID: PMC6953983 DOI: 10.1016/bs.mie.2018.09.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Metabolomics aims at the comprehensive identification of metabolites in complex mixtures to characterize the state of a biological system and elucidate their roles in biochemical pathways. For many biological samples, a large number of spectral features observed by NMR spectroscopy and mass spectrometry (MS) belong to unknowns, i.e., these features do not belong to metabolites that have been previously identified, and their spectral information is not available in databases. By combining NMR, MS, and combinatorial cheminformatics, the analysis of unknowns can be pursued in complex mixtures requiring minimal purification. This chapter describes the SUMMIT MS/NMR approach covering sample preparation, NMR and MS data collection and processing, and the identification of likely unknowns with the use of cheminformatics tools and the prediction of NMR spectral properties.
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Affiliation(s)
- Abigail Leggett
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio 43210, U.S.A
| | - Cheng Wang
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, U.S.A
| | - Da-Wei Li
- Campus Chemical Instrument Center (CCIC), The Ohio State University, Columbus, Ohio 43210, U.S.A
| | - Arpad Somogyi
- Campus Chemical Instrument Center (CCIC), The Ohio State University, Columbus, Ohio 43210, U.S.A
| | - Lei Bruschweiler-Li
- Campus Chemical Instrument Center (CCIC), The Ohio State University, Columbus, Ohio 43210, U.S.A
| | - Rafael Brüschweiler
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio 43210, U.S.A.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, U.S.A.,Campus Chemical Instrument Center (CCIC), The Ohio State University, Columbus, Ohio 43210, U.S.A.,Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, U.S.A.,To whom correspondence should be addressed: Rafael Brüschweiler, Ph.D.,
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44
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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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Zelentsova EA, Yanshole VV, Tsentalovich YP. A novel method of sample homogenization with the use of a microtome-cryostat apparatus. RSC Adv 2019; 9:37809-37817. [PMID: 35541765 PMCID: PMC9075820 DOI: 10.1039/c9ra06808b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/14/2019] [Indexed: 12/17/2022] Open
Abstract
Quantitative metabolomics places high demands on sample preparation, including a high degree of metabolite extraction and controlled sample weight. In respect to elastic collagen-rich tissues, the existing methods of sample homogenization poorly fit these demands due to incomplete homogenization, sample material loss, or metabolite degradation. Herein, a novel method based on the use of a microtome-cryostat apparatus is proposed. The performance of the cryotome method is compared with the results obtained with the use of a vortex bead beating. NMR-based metabolomic analysis shows that the extraction efficiency and the data scattering for both methods of sample preparation are similar. However, the heat generation during the bead beating causes the destruction of thermally-unstable compounds; besides, it may cause protein hydrolysis, leading to an artificial increase in the amino acid level. The cryotome method of sample homogenization does not cause sample heating, and it seems to be ideal for elastic tissues. A novel method of homogenization of elastic tissues does not cause sample heating and material losses.![]()
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Affiliation(s)
- Ekaterina A. Zelentsova
- International Tomography Center SB RAS
- Novosibirsk 630090
- Russia
- Novosibirsk State University
- Novosibirsk 630090
| | - Vadim V. Yanshole
- International Tomography Center SB RAS
- Novosibirsk 630090
- Russia
- Novosibirsk State University
- Novosibirsk 630090
| | - Yuri P. Tsentalovich
- International Tomography Center SB RAS
- Novosibirsk 630090
- Russia
- Novosibirsk State University
- Novosibirsk 630090
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46
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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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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. Hochdurchsatz‐Metabolomik mit 1D‐NMR. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201804736] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P. Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Veronica Ghini
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Gaia Meoni
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Cristina Licari
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of Florence Largo Brambilla 3 Florence Italien
| | - Paola Turano
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
| | - Claudio Luchinat
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
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Bhinderwala F, Wase N, DiRusso C, Powers R. Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation. J Proteome Res 2018; 17:4017-4022. [PMID: 30303385 DOI: 10.1021/acs.jproteome.8b00567] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.
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Affiliation(s)
- Fatema Bhinderwala
- Department of Chemistry , University of Nebraska , Lincoln , Nebraska 68588-0304 , United States.,Nebraska Center for Integrated Biomolecular Communication , Lincoln , Nebraska 68588-0304 , United States
| | - Nishikant Wase
- Department of Biochemistry , University of Nebraska , Lincoln , Nebraska 68588-0664 , United States
| | - Concetta DiRusso
- Department of Biochemistry , University of Nebraska , Lincoln , Nebraska 68588-0664 , United States.,Nebraska Center for Integrated Biomolecular Communication , Lincoln , Nebraska 68588-0304 , United States
| | - Robert Powers
- Department of Chemistry , University of Nebraska , Lincoln , Nebraska 68588-0304 , United States.,Nebraska Center for Integrated Biomolecular Communication , Lincoln , Nebraska 68588-0304 , United States
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49
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Li D, Hansen AL, Bruschweiler-Li L, Brüschweiler R. Non-Uniform and Absolute Minimal Sampling for High-Throughput Multidimensional NMR Applications. Chemistry 2018; 24:11535-11544. [PMID: 29566285 PMCID: PMC6488043 DOI: 10.1002/chem.201800954] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Indexed: 11/10/2022]
Abstract
Many biomolecular NMR applications can benefit from the faster acquisition of multidimensional NMR data with high resolution and their automated analysis and interpretation. In recent years, a number of non-uniform sampling (NUS) approaches have been introduced for the reconstruction of multidimensional NMR spectra, such as compressed sensing, thereby bypassing traditional Fourier-transform processing. Such approaches are applicable to both biomacromolecules and small molecules and their complex mixtures and can be combined with homonuclear decoupling (pure shift) and covariance processing. For homonuclear 2D TOCSY experiments, absolute minimal sampling (AMS) permits the drastic shortening of measurement times necessary for high-throughput applications for identification and quantification of components in complex biological mixtures in the field of metabolomics. Such TOCSY spectra can be comprehensively represented by graphic theoretical maximal cliques for the identification of entire spin systems and their subsequent query against NMR databases. Integration of these methods in webservers permits the rapid and reliable identification of mixture components. Recent progress is reviewed in this Minireview.
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Affiliation(s)
- Dawei Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Alexandar L Hansen
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Lei Bruschweiler-Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Rafael Brüschweiler
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio, 43210, USA
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, 43210, USA
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio, 43210, USA
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50
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Ghosh S, Pathak S, Sonawat HM, Sharma S, Sengupta A. Metabolomic changes in vertebrate host during malaria disease progression. Cytokine 2018; 112:32-43. [PMID: 30057363 DOI: 10.1016/j.cyto.2018.07.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 07/16/2018] [Accepted: 07/17/2018] [Indexed: 12/24/2022]
Abstract
Metabolomics refers to top-down systems biological analysis of metabolites in biological specimens. Phenotypic proximity of metabolites makes them interesting candidates for studying biomarkers of environmental stressors such as parasitic infections. Moreover, the host-parasite interaction directly impinges upon metabolic pathways since the parasite uses the host metabolite pool as a biosynthetic resource. Malarial infection, although not recognized as a classic metabolic disorder, often leads to severe metabolic changes such as hypoglycemia and lactic acidosis. Thus, metabolomic analysis of the infection has become an invaluable tool for promoting a better understanding of the host-parasite interaction and for the development of novel therapeutics. In this review, we summarize the current knowledge obtained from metabolomic studies of malarial infection in rodent models and human patients. Metabolomic analysis of experimental rodent malaria has provided significant insights into the mechanisms of disease progression including utilization of host resources by the parasite, sexual dimorphism in metabolic phenotypes, and cellular changes in host metabolism. Moreover, these studies also provide proof of concept for prediction of cerebral malaria. On the other hand, metabolite analysis of patient biofluids generates extensive data that could be of use in identifying biomarkers of infection severity and in monitoring disease progression. Through the use of metabolomic datasets one hopes to assess crucial infection-specific issues such as clinical severity, drug resistance, therapeutic targets, and biomarkers. Also discussed are nascent or newly emerging areas of metabolomics such as pre-erythrocytic stages of the infection and the host immune response. This review is organized in four broad sections-methodologies for metabolomic analysis, rodent infection models, studies of human clinical specimens, and potential of immunometabolomics. Data summarized in this review should serve as a springboard for novel hypothesis testing and lead to a better understanding of malarial infection and parasite biology.
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Affiliation(s)
- Soumita Ghosh
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA.
| | - Sulabha Pathak
- Department of Biological Sciences, Tata Institute of Fundamental Research, 1, Homi Bhabha Road, Mumbai 400005, India
| | - Haripalsingh M Sonawat
- Department of Chemical Sciences, Tata Institute of Fundamental Research, 1, Homi Bhabha Road, Mumbai 400005, India
| | - Shobhona Sharma
- Department of Biological Sciences, Tata Institute of Fundamental Research, 1, Homi Bhabha Road, Mumbai 400005, India
| | - Arjun Sengupta
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA.
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