1
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Hilovsky D, Hartsell J, Young JD, Liu X. Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies. Metabolites 2024; 14:318. [PMID: 38921453 PMCID: PMC11205609 DOI: 10.3390/metabo14060318] [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: 05/07/2024] [Revised: 05/13/2024] [Accepted: 05/28/2024] [Indexed: 06/27/2024] Open
Abstract
Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.
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Affiliation(s)
- Dalton Hilovsky
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695, USA; (D.H.); (J.H.)
| | - Joshua Hartsell
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695, USA; (D.H.); (J.H.)
| | - Jamey D. Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37212, USA
| | - Xiaojing Liu
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695, USA; (D.H.); (J.H.)
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2
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Hu J, Bettembourg M, Xue L, Hu R, Schnürer A, Sun C, Jin Y, Sundström JF. A low-methane rice with high-yield potential realized via optimized carbon partitioning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170980. [PMID: 38373456 DOI: 10.1016/j.scitotenv.2024.170980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/25/2024] [Accepted: 02/13/2024] [Indexed: 02/21/2024]
Abstract
Global rice cultivation significantly contributes to anthropogenic methane emissions. The methane emissions are caused by methane-producing microorganisms (methanogenic archaea) that are favoured by the anoxic conditions of paddy soils and small carbon molecules released from rice roots. However, different rice cultivars are associated with differences in methane emission rates suggesting that there is a considerable natural variation in this trait. Starting from the hypothesis that sugar allocation within a plant is an important factor influencing both yields and methane emissions, the aim of this study was to produce high-yielding rice lines associated with low methane emissions. In this study, the offspring (here termed progeny lines) of crosses between a newly characterized low-methane rice variety, Heijing 5, and three high-yielding elite varieties, Xiushui, Huayu and Jiahua, were selected for combined low-methane and high-yield properties. Analyses of total organic carbon and carbohydrates showed that the progeny lines stored more carbon in above-ground tissues than the maternal elite varieties. Also, metabolomic analysis of rhizospheric soil surrounding the progeny lines showed reduced levels of glucose and other carbohydrates. The carbon allocation, from roots to shoots, was further supported by a transcriptome analysis using massively parallel sequencing of mRNAs that demonstrated elevated expression of the sugar transporters SUT-C and SWEET in the progeny lines as compared to the parental varieties. Furthermore, measurement of methane emissions from plants, grown in greenhouse as well as outdoor rice paddies, showed a reduction in methane emissions by approximately 70 % in the progeny lines compared to the maternal elite varieties. Taken together, we report here on three independent low-methane-emission rice lines with high yield potential. We also provide a first molecular characterisation of the progeny lines that can serve as a foundation for further studies of candidate genes involved in sugar allocation and reduced methane emissions from rice cultivation.
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Affiliation(s)
- Jia Hu
- Department of Plant Biology, Sweden University of Agricultural Science, The Linnean Centre for Plant Biology, Box 7080, SE-75007 Uppsala, Sweden
| | - Mathilde Bettembourg
- Department of Plant Biology, Sweden University of Agricultural Science, The Linnean Centre for Plant Biology, Box 7080, SE-75007 Uppsala, Sweden
| | - Lihong Xue
- Key Laboratory of Agro-environment in Downstream of Yangtze plain, Ministry of Agriculture and Rural Affairs of China, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Ronggui Hu
- College of Resources and Environment, Huazhong Agricultural University, Wuhan 43070, China
| | - Anna Schnürer
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Box 7015, SE-750 07 Uppsala, Sweden
| | - Chuanxin Sun
- Department of Plant Biology, Sweden University of Agricultural Science, The Linnean Centre for Plant Biology, Box 7080, SE-75007 Uppsala, Sweden
| | - Yunkai Jin
- Department of Plant Biology, Sweden University of Agricultural Science, The Linnean Centre for Plant Biology, Box 7080, SE-75007 Uppsala, Sweden
| | - Jens F Sundström
- Department of Plant Biology, Sweden University of Agricultural Science, The Linnean Centre for Plant Biology, Box 7080, SE-75007 Uppsala, Sweden.
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Domżał B, Nawrocka EK, Gołowicz D, Ciach MA, Miasojedow B, Kazimierczuk K, Gambin A. Magnetstein: An Open-Source Tool for Quantitative NMR Mixture Analysis Robust to Low Resolution, Distorted Lineshapes, and Peak Shifts. Anal Chem 2024; 96:188-196. [PMID: 38117933 PMCID: PMC10782418 DOI: 10.1021/acs.analchem.3c03594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/22/2023]
Abstract
1H NMR spectroscopy is a powerful tool for analyzing mixtures including determining the concentrations of individual components. When signals from multiple compounds overlap, this task requires computational solutions. They are typically based on peak-picking and the comparison of obtained peak lists with libraries of individual components. This can fail if peaks are not sufficiently resolved or when peak positions differ between the library and the mixture. In this paper, we present Magnetstein, a quantification algorithm rooted in the optimal transport theory that makes it robust to unexpected frequency shifts and overlapping signals. Thanks to this, Magnetstein can quantitatively analyze difficult spectra with the estimation trueness an order of magnitude higher than that of commercial tools. Furthermore, the method is easier to use than other approaches, having only two parameters with default values applicable to a broad range of experiments and requiring little to no preprocessing of the spectra.
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Affiliation(s)
- Barbara Domżał
- Faculty
of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, Warsaw 02-097, Poland
| | - Ewa Klaudia Nawrocka
- Centre
of New Technologies, University of Warsaw, Banacha 2C, Warsaw 02-097, Poland
| | - Dariusz Gołowicz
- Institute
of Physical Chemistry, Polish Academy of
Sciences, Kasprzaka 44/52, Warsaw 01-224, Poland
| | - Michał Aleksander Ciach
- Faculty
of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, Warsaw 02-097, Poland
| | - Błażej Miasojedow
- Faculty
of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, Warsaw 02-097, Poland
| | | | - Anna Gambin
- Faculty
of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, Warsaw 02-097, Poland
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4
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Alexandersson E, Sandström C, Meijer J, Nestor G, Broberg A, Röhnisch HE. Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates. Metabolomics 2023; 20:11. [PMID: 38141081 DOI: 10.1007/s11306-023-02073-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy. OBJECTIVES To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals. METHODS An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix. RESULTS The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated. CONCLUSIONS The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.
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Affiliation(s)
- Elin Alexandersson
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
| | - Corine Sandström
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Johan Meijer
- Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Gustav Nestor
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Anders Broberg
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
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5
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Rout M, Lipfert M, Lee BL, Berjanskii M, Assempour N, Fresno RV, Cayuela AS, Dong Y, Johnson M, Shahin H, Gautam V, Sajed T, Oler E, Peters H, Mandal R, Wishart DS. MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:681-704. [PMID: 37265034 DOI: 10.1002/mrc.5371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/12/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023]
Abstract
Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.
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Affiliation(s)
- Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Nazanin Assempour
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rosa Vazquez Fresno
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Arnau Serra Cayuela
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Ying Dong
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Mathew Johnson
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Honeya Shahin
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rupasri Mandal
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
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6
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Takis PG, Aggelidou VA, Sands CJ, Louka A. Mapping of 1 H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:759-769. [PMID: 37666776 PMCID: PMC10946494 DOI: 10.1002/mrc.5392] [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: 12/07/2022] [Revised: 08/03/2023] [Accepted: 08/15/2023] [Indexed: 09/06/2023]
Abstract
One-dimensional (1D) proton-nuclear magnetic resonance (1 H-NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large-scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma-EDTA spectra, were able to successfully predict the 1 H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv1/2 ) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of 1 H NMR chemical shifts based solely on chemically defined constraints.
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Affiliation(s)
- Panteleimon G. Takis
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and ReproductionImperial College LondonLondonUK
- National Phenome Centre, Department of Metabolism, Digestion and ReproductionImperial College LondonLondonUK
| | - Varvara A. Aggelidou
- Department of Biological Applications and TechnologiesUniversity of IoanninaIoanninaGreece
| | - Caroline J. Sands
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and ReproductionImperial College LondonLondonUK
- National Phenome Centre, Department of Metabolism, Digestion and ReproductionImperial College LondonLondonUK
| | - Alexandra Louka
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of NeurologyUniversity College LondonLondonUK
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7
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Peng Z, Zhang W, Zhang X, Mao J, Zhang Q, Zhao W, Zhang S, Xie J. Recent advances in analysis of capsaicin and its effects on metabolic pathways by mass spectrometry. Front Nutr 2023; 10:1227517. [PMID: 37575327 PMCID: PMC10419207 DOI: 10.3389/fnut.2023.1227517] [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: 05/23/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023] Open
Abstract
Capsaicin is the main food active component in Capsicum that has gained considerable attention due to its broad biological activities, including antioxidation, anti-inflammation, anti-tumor, weight regulation, cardiac protection, anti-calculi, and diurnal-circadian regulation. The potent biological effects of capsaicin are intimately related to metabolic pathways such as lipid metabolism, energy metabolism, and antioxidant stress. Mass spectrometry (MS) has emerged as an effective tool for deciphering the mechanisms underlying capsaicin metabolism and its biological impacts. However, it remains challenging to accurately identify and quantify capsaicin and its self-metabolites in complex food and biological samples, and to integrate multi-omics data generated from MS. In this work, we summarized recent advances in the detection of capsaicin and its self-metabolites using MS and discussed the relevant MS-based studies of metabolic pathways. Furthermore, we discussed current issues and future directions in this field. In-depth studies of capsaicin metabolism and its physiological functions based on MS is anticipated to yield new insights and methods for preventing and treating a wide range of diseases.
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Affiliation(s)
- Zifang Peng
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
| | - Wenfen Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
| | - Xu Zhang
- School of Ecology and Environment, Zhengzhou University, Zhengzhou, Henan, China
| | - Jian Mao
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
| | - Qidong Zhang
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
| | - Wuduo Zhao
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
| | - Shusheng Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
| | - Jianping Xie
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
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8
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Alonso-Moreno P, Rodriguez I, Izquierdo-Garcia JL. Benchtop NMR-Based Metabolomics: First Steps for Biomedical Application. Metabolites 2023; 13:metabo13050614. [PMID: 37233655 DOI: 10.3390/metabo13050614] [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: 12/30/2022] [Revised: 04/19/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.
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Affiliation(s)
- Pilar Alonso-Moreno
- NMR and Imaging in Biomedicine Group, Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Ignacio Rodriguez
- NMR and Imaging in Biomedicine Group, Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040 Madrid, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Jose Luis Izquierdo-Garcia
- NMR and Imaging in Biomedicine Group, Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040 Madrid, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
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9
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Guo Z, Gu J, Zhang M, Su F, Su W, Xie Y. NMR-Based Metabolomics to Analyze the Effects of a Series of Monoamine Oxidases-B Inhibitors on U251 Cells. Biomolecules 2023; 13:biom13040600. [PMID: 37189348 DOI: 10.3390/biom13040600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Alzheimer’s disease (AD) is a typical progressive neurodegenerative disorder, and with multiple possible pathogenesis. Among them, coumarin derivatives could be used as potential drugs as monoamine oxidase-B (MAO-B) inhibitors. Our lab has designed and synthesized coumarin derivatives based on MAO-B. In this study, we used nuclear magnetic resonance (NMR)-based metabolomics to accelerate the pharmacodynamic evaluation of candidate drugs for coumarin derivative research and development. We detailed alterations in the metabolic profiles of nerve cells with various coumarin derivatives. In total, we identified 58 metabolites and calculated their relative concentrations in U251 cells. In the meantime, the outcomes of multivariate statistical analysis showed that when twelve coumarin compounds were treated with U251cells, the metabolic phenotypes were distinct. In the treatment of different coumarin derivatives, there several metabolic pathways changed, including aminoacyl-tRNA biosynthesis, D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism and valine, leucine and isoleucine biosynthesis. Our work documented how our coumarin derivatives affected the metabolic phenotype of nerve cells in vitro. We believe that these NMR-based metabolomics might accelerate the process of drug research in vitro and in vivo.
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10
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Wang W, Ma LH, Maletic-Savatic M, Liu Z. NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.01.530642. [PMID: 36909516 PMCID: PMC10002723 DOI: 10.1101/2023.03.01.530642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.
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Affiliation(s)
- Wanli Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Graduate Program of Quantitative & Computational Biosciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Li-Hua Ma
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Mirjana Maletic-Savatic
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
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11
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Li DW, Bruschweiler-Li L, Hansen A, Brüschweiler R. DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra. MAGNETIC RESONANCE (GOTTINGEN, GERMANY) 2023; 4:19-26. [PMID: 37904796 PMCID: PMC10539790 DOI: 10.5194/mr-4-19-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/16/2022] [Indexed: 11/01/2023]
Abstract
The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.
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Affiliation(s)
- Da-Wei Li
- 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
| | - Alexandar L. Hansen
- 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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12
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [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] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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13
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de Medeiros LS, de Araújo Júnior MB, Peres EG, da Silva JCI, Bassicheto MC, Di Gioia G, Veiga TAM, Koolen HHF. Discovering New Natural Products Using Metabolomics-Based Approaches. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:185-224. [PMID: 37843810 DOI: 10.1007/978-3-031-41741-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.
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Affiliation(s)
- Lívia Soman de Medeiros
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil.
| | - Moysés B de Araújo Júnior
- Grupo de Pesquisa em Metabolômica e Espectrometria de Massas, Universidade do Estado do Amazonas, Manaus, Brazil
| | - Eldrinei G Peres
- Grupo de Pesquisa em Metabolômica e Espectrometria de Massas, Universidade do Estado do Amazonas, Manaus, Brazil
| | | | - Milena Costa Bassicheto
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | - Giordanno Di Gioia
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | - Thiago André Moura Veiga
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
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14
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Pillai MS, Paritala ST, Shah RP, Sharma N, Sengupta P. Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics. Drug Metab Rev 2022; 54:401-426. [PMID: 36351878 DOI: 10.1080/03602532.2022.2125987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.
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Affiliation(s)
- Megha Sajakumar Pillai
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Sree Teja Paritala
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Ravi P Shah
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Nitish Sharma
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Pinaki Sengupta
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
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15
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Brunel M, Burkina V, Pickova J, Sampels S, Moazzami AA. Oleaginous yeast Rhodotorula toruloides biomass effect on the metabolism of Arctic char ( Salvelinus alpinus). Front Mol Biosci 2022; 9:931946. [PMID: 36052171 PMCID: PMC9425082 DOI: 10.3389/fmolb.2022.931946] [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: 04/29/2022] [Accepted: 07/11/2022] [Indexed: 12/30/2022] Open
Abstract
Sustainability issues arise when using fish oil and vegetable oils in fish feed production for aquaculture purposes. Microbial production of single cell oil is a potential alternative as a lipid ingredient in the production of fish feed. In this study, we replaced the vegetable oils with the oleaginous yeast R. toruloides biomass in the diet of Arctic char (S. alpinus) and investigated the effects on health and composition. Measurement of fish growth parameters showed a higher liver weight and hepatosomatic index in the experimental group of fish fed partly with yeast biomass compared to a control group fed a diet with vegetable oils. No significant differences in the lipid content of muscle and liver tissues were found. The fatty acid profiles in the muscle of both fish groups were similar while the experimental fish group had a higher amount of monounsaturated fatty acids in the liver. Histology of livers showed no significant difference in the number of lipid droplets. The size of hepatic lipid droplets seemed to be related to liver fat content. Quantification of metabolites in the liver revealed no differences between the fish groups while plasma metabolites involved in energy pathways such as alanine, 3-hydroxybutyrate, creatinine, serine, betaine, and choline were significantly higher in the experimental fish group.
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Affiliation(s)
- Mathilde Brunel
- Department of Molecular Sciences, Faculty of Natural Resources and Agricultural Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden,*Correspondence: Mathilde Brunel,
| | - Viktoriia Burkina
- Department of Molecular Sciences, Faculty of Natural Resources and Agricultural Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden,Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Research Institute of Fish Culture and Hydrobiology, University of South Bohemia in České Budějovice, Vodňany, Czech Republic
| | - Jana Pickova
- Department of Molecular Sciences, Faculty of Natural Resources and Agricultural Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Sabine Sampels
- Department of Molecular Sciences, Faculty of Natural Resources and Agricultural Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Ali A. Moazzami
- Department of Molecular Sciences, Faculty of Natural Resources and Agricultural Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
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16
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Wishart DS, Cheng LL, Copié V, Edison AS, Eghbalnia HR, Hoch JC, Gouveia GJ, Pathmasiri W, Powers R, Schock TB, Sumner LW, Uchimiya M. NMR and Metabolomics-A Roadmap for the Future. Metabolites 2022; 12:678. [PMID: 35893244 PMCID: PMC9394421 DOI: 10.3390/metabo12080678] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 12/03/2022] Open
Abstract
Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.
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Affiliation(s)
- David S. Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Leo L. Cheng
- Department of Pathology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Valérie Copié
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59715, USA;
| | - Arthur S. Edison
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602-0001, USA
| | - Hamid R. Eghbalnia
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA; (H.R.E.); (J.C.H.)
| | - Jeffrey C. Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA; (H.R.E.); (J.C.H.)
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602-0001, USA
| | - Wimal Pathmasiri
- Nutrition Research Institute, Department of Nutrition, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Tracey B. Schock
- National Institute of Standards and Technology (NIST), Chemical Sciences Division, Charleston, SC 29412, USA;
| | - Lloyd W. Sumner
- Interdisciplinary Plant Group, MU Metabolomics Center, Bond Life Sciences Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Mario Uchimiya
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
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17
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Li DW, Leggett A, Bruschweiler-Li L, Brüschweiler R. COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples. Anal Chem 2022; 94:8674-8682. [PMID: 35672005 PMCID: PMC9218957 DOI: 10.1021/acs.analchem.2c00891] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D 13C-1H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D 13C-1H HSQC and optionally 2D 1H-1H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.
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Affiliation(s)
- Da-Wei Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Abigail Leggett
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.,Ohio State Biochemistry Program, 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
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States.,Department of Chemistry and Biochemistry, 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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18
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Wang X, Mickiewicz B, Thompson GC, Joffe AR, Blackwood J, Vogel HJ, Kopciuk KA. Comparison of Two Automated Targeted Metabolomics Programs to Manual Profiling by an Experienced Spectroscopist for 1H-NMR Spectra. Metabolites 2022; 12:metabo12030227. [PMID: 35323670 PMCID: PMC8949809 DOI: 10.3390/metabo12030227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/26/2022] [Accepted: 03/02/2022] [Indexed: 12/10/2022] Open
Abstract
Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student’s t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7–86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.
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Affiliation(s)
- Xiangyu Wang
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Beata Mickiewicz
- Department of Pediatrics, Cumming School of Medicine and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada;
| | - Graham C. Thompson
- Departments of Pediatrics and Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada;
| | - Ari R. Joffe
- Department of Pediatrics, Division of Pediatric Critical Care, University of Alberta, Edmonton, AB T6G 1C9, Canada;
| | - Jaime Blackwood
- Department of PICU and Critical Care, Alberta Children’s Hospital, Alberta Health Services, Calgary, AB T3B 6A8, Canada;
| | - Hans J. Vogel
- Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary, AB T2N 1N4, Canada
- Correspondence:
| | - Karen A. Kopciuk
- Departments of Community Health Sciences, Mathematics and Statistics, and Oncology, University of Calgary, Calgary, AB T2N 1N4, Canada;
- Cancer Epidemiology and Prevention Research, Alberta Health Sciences, Calgary, AB T2S 3C3, Canada
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19
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Hana CA, Tran LV, Mölzer C, Müllner E, Hörmann-Wallner M, Franzke B, Tosevska A, Zöhrer PA, Doberer D, Marculescu R, Bulmer AC, Freisling H, Moazzami AA, Wagner KH. Serum metabolomics analysis reveals increased lipid catabolism in mildly hyperbilirubinemic Gilbert's syndrome individuals. Metabolism 2021; 125:154913. [PMID: 34653509 DOI: 10.1016/j.metabol.2021.154913] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/12/2021] [Accepted: 10/07/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND The protective role of mildly elevated bilirubin against CVD and diabetes mellitus type 2 (DMT2) is associated with a favorable lipid phenotype. As the mechanistic understanding of this protection in humans remains elusive, we aimed to assess the metabolomics profile of mildly hyperbilirubinemic (Gilbert's syndrome; GS) individuals especially targeting lipid catabolism. METHODS AND RESULTS Using NMR serum metabolomics of 56 GS individuals and 56 age and gender-matched healthy controls, GS individuals demonstrated significantly greater concentrations of acetylcarnitine (+20%, p < 0.001) and the ketone bodies, 3-hydroxybutyric acid (+132%, p < 0.001), acetoacetic acid (+95%, p < 0.001) and acetone (+46%, p < 0.001). Metabolites associated with an increased mitochondrial lipid metabolism such as citrate (+15%, p < 0.001), anaplerotic amino acid intermediates and creatinine were significantly greater and creatine significantly reduced in GS individuals. Stimulators of lipid catabolism including AMPK (+59%, p < 0.001), pPPARα (+24%, p < 0.001) and T3 (+9%, p = 0.009) supported the metabolomics data while concomitantly blood glucose and insulin (-33%, p = 0.002) levels were significantly reduced. We further showed that the increased lipid catabolism partially mediates the favorable lipid phenotype (lower triglycerides) of GS individuals. Increased trimethylamine (+35%, p < 0.001) indicated changes in trimethylamine metabolism, an emerging predictor of metabolic health. CONCLUSION We showed an enhanced lipid catabolism in mildly hyperbilirubinemic individuals, novel evidence as to why these individuals are leaner and protected against chronic metabolic diseases emphasizing bilirubin to be a promising future target in obese and dyslipidemia patients.
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Affiliation(s)
- Claudia A Hana
- Faculty of Lifesciences, Department of Nutritional Sciences, University of Vienna, Vienna, Austria.
| | - Lan V Tran
- Faculty of Lifesciences, Department of Nutritional Sciences, University of Vienna, Vienna, Austria
| | - Christine Mölzer
- School of Medicine, Institute of Medical Sciences, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Elisabeth Müllner
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Marlies Hörmann-Wallner
- Institute for Dietetics and Nutrition, University of Applied Sciences FH JOANNEUM, Graz, Austria
| | - Bernhard Franzke
- Faculty of Lifesciences, Department of Nutritional Sciences, University of Vienna, Vienna, Austria; Research Platform Active Ageing, University of Vienna, Vienna, Austria
| | - Anela Tosevska
- Faculty of Lifesciences, Department of Nutritional Sciences, University of Vienna, Vienna, Austria; Internal Medicine III, Division of Rheumatology, Medical University of Vienna; Vienna, Austria
| | - Patrick A Zöhrer
- Faculty of Lifesciences, Department of Nutritional Sciences, University of Vienna, Vienna, Austria; Research Platform Active Ageing, University of Vienna, Vienna, Austria
| | - Daniel Doberer
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Rodrig Marculescu
- Clinical Institute of Laboratory Medicine, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Andrew C Bulmer
- School of Medical Science and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
| | - Heinz Freisling
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Karl-Heinz Wagner
- Faculty of Lifesciences, Department of Nutritional Sciences, University of Vienna, Vienna, Austria; Research Platform Active Ageing, University of Vienna, Vienna, Austria.
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20
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He X, Gu J, Zou D, Yang H, Zhang Y, Ding Y, Teng L. NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer. Front Mol Biosci 2021; 8:708052. [PMID: 34796199 PMCID: PMC8592909 DOI: 10.3389/fmolb.2021.708052] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.
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Affiliation(s)
- Xiangming He
- The First Affiliated Hospital, Zhejiang University School of Medicine (FAHZU), Hangzhou, China.,Chinese Academy of Sciences, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Jinping Gu
- Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, China
| | - Dehong Zou
- Chinese Academy of Sciences, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Hongjian Yang
- Chinese Academy of Sciences, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Yongfang Zhang
- Chinese Academy of Sciences, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Yuqing Ding
- Chinese Academy of Sciences, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Lisong Teng
- The First Affiliated Hospital, Zhejiang University School of Medicine (FAHZU), Hangzhou, China
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21
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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: 36] [Impact Index Per Article: 12.0] [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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22
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Müller B, Rasmusson AJ, Just D, Jayarathna S, Moazzami A, Novicic ZK, Cunningham JL. Fecal Short-Chain Fatty Acid Ratios as Related to Gastrointestinal and Depressive Symptoms in Young Adults. Psychosom Med 2021; 83:693-699. [PMID: 34267089 PMCID: PMC8428857 DOI: 10.1097/psy.0000000000000965] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 04/14/2021] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Short-chain fatty acids (SCFAs) are produced by the gut microbiota and may reflect health. Gut symptoms are common in individuals with depressive disorders, and recent data indicate relationships between gut microbiota and psychiatric health. We aimed to investigate potential associations between SCFAs and self-reported depressive and gut symptoms in young adults. METHODS Fecal samples from 164 individuals (125 were patients with psychiatric disorders: mean [standard deviation] age = 21.9 [2.6] years, 14% men; 39 nonpsychiatric controls: age = 28.5 [9.5] years, 38% men) were analyzed for the SCFA acetate, butyrate, and propionate by nuclear magnetic resonance spectroscopy. We then compared SCFA ratios with dimensional measures of self-reported depressive and gut symptoms. RESULTS Depressive symptoms showed a positive association with acetate levels (ρ = 0.235, p = .003) and negative associations with both butyrate (ρ = -0.195, p = .014) and propionate levels (ρ = -0.201, p = .009) in relation to total SCFA levels. Furthermore, symptoms of diarrhea showed positive associations with acetate (ρ = 0.217, p = .010) and negative associations with propionate in relation to total SCFA levels (ρ = 0.229, p = 0-007). Cluster analysis revealed a heterogeneous pattern where shifts in SCFA ratios were observed in individuals with elevated levels of depressive symptoms, elevated levels of gut symptoms, or both. CONCLUSIONS Shifts in SCFAs are associated with both depressive symptoms and gut symptoms in young adults and may have of relevance for treatment.
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Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor. Metabolites 2021; 11:metabo11080537. [PMID: 34436478 PMCID: PMC8401204 DOI: 10.3390/metabo11080537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 01/03/2023] Open
Abstract
Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).
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Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
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Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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25
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Tinte MM, Chele KH, van der Hooft JJJ, Tugizimana F. Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview. Metabolites 2021; 11:445. [PMID: 34357339 PMCID: PMC8305945 DOI: 10.3390/metabo11070445] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 12/27/2022] Open
Abstract
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.
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Affiliation(s)
- Morena M. Tinte
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | - Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | | | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
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Röhnisch HE, Eriksson J, Tran LV, Müllner E, Sandström C, Moazzami AA. Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma. Anal Chem 2021; 93:8729-8738. [PMID: 34128648 PMCID: PMC8253485 DOI: 10.1021/acs.analchem.0c04233] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
We have recently
presented an Automated Quantification Algorithm
(AQuA) and demonstrated its utility for rapid and accurate absolute
metabolite quantification in 1H NMR spectra in which positions
and line widths of signals were predicted from a constant metabolite
spectral library. The AQuA quantifies based on one preselected signal
per metabolite and employs library spectra to model interferences
from other metabolite signals. However, for some types of spectra,
the interspectral deviations of signal positions and line widths can
be pronounced; hence, interferences cannot be modeled using a constant
spectral library. We here address this issue and present an improved
AQuA that handles interspectral deviations. The improved AQuA monitors
and characterizes the appearance of specific signals in each spectrum
and automatically adjusts the spectral library to model interferences
accordingly. The performance of the improved AQuA was tested on a
large data set from plasma samples collected using ethylenediaminetetraacetic
acid (EDTA) as an anticoagulant (n = 772). These
spectra provided a suitable test system for the improved AQuA since
EDTA signals (i) vary in intensity, position, and line width between
spectra and (ii) interfere with many signals from plasma metabolites
targeted for quantification (n = 54). Without the
improvement, ca. 20 out of the 54 metabolites would have been overestimated.
This included acetylcarnitine and ornithine, which are considered
particularly difficult to quantify with 1H NMR in EDTA-containing
plasma. Furthermore, the improved AQuA performed rapidly (<10 s
for all spectra). We believe that the improved AQuA provides a basis
for automated quantification in other data sets where specific signals
show interspectral deviations.
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Affiliation(s)
- Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Jan Eriksson
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Lan V Tran
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Elisabeth Müllner
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Corine Sandström
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
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Differences in metabolic profiles between the Burmese, the Maine coon and the Birman cat-Three breeds with varying risk for diabetes mellitus. PLoS One 2021; 16:e0249322. [PMID: 33886598 PMCID: PMC8062062 DOI: 10.1371/journal.pone.0249322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 03/15/2021] [Indexed: 12/31/2022] Open
Abstract
Feline diabetes mellitus shares many features with type 2 diabetes in people, regarding clinical presentation, physiology, and pathology. A breed predisposition for type 2 diabetes has been identified, with the Burmese breed at a fivefold increased risk of developing the condition compared to other purebred cats. We aimed to characterize the serum metabolome in cats (n = 63) using nuclear magnetic resonance metabolomics, and to compare the metabolite pattern of Burmese cats with that of two cat breeds of medium or low risk of diabetes, the Maine coon (MCO) and Birman cat, respectively. Serum concentrations of adiponectin, insulin and insulin-like growth factor-1 were also measured (n = 94). Burmese cats had higher insulin and lower adiponectin concentrations than MCO cats. Twenty one metabolites were discriminative between breeds using a multivariate statistical approach and 15 remained significant after adjustment for body weight and body condition score. Burmese cats had higher plasma levels of 2-hydroxybutyrate relative to MCO and Birman cats and increased concentrations of 2-oxoisocaproic acid, and tyrosine, and lower concentrations of dimethylglycine relative to MCO cats. The metabolic profile of MCO cats was characterized by high concentrations of arginine, asparagine, methionine, succinic acid and low levels of acetylcarnitine while Birman cats had the highest creatinine and the lowest taurine plasma levels, compared with MCO and Burmese. The pattern of metabolites in Burmese cats is similar to that in people with insulin resistance. In conclusion, the metabolic profile differed between healthy cats of three breeds. Detection of an abnormal metabolome might identify cats at risk of developing diabetes.
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Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring. Metabolites 2021; 11:metabo11030157. [PMID: 33803350 PMCID: PMC8001003 DOI: 10.3390/metabo11030157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/24/2021] [Accepted: 03/05/2021] [Indexed: 12/23/2022] Open
Abstract
High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.
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29
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Mal TK, Tian Y, Patterson AD. Sample Preparation and Data Analysis for NMR-Based Metabolomics. Methods Mol Biol 2021; 2194:301-313. [PMID: 32926373 DOI: 10.1007/978-1-0716-0849-4_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
NMR spectroscopy has become one of the preferred analytical techniques for metabolomics studies due to its inherent nondestructive nature, ability to identify and quantify metabolites simultaneously in a complex mixture, minimal sample preparation requirement, and high degree of experimental reproducibility. NMR-based metabolomics studies involve the measurement and multivariate statistical analysis of metabolites present in biological samples such as biofluids, stool/feces, intestinal content, tissue, and cell extracts by high-resolution NMR spectroscopy-the goal then is to identify and quantify metabolites and evaluate changes of metabolite concentrations in response to some perturbation. Here we describe methodologies for NMR sample preparation of biofluids (serum, saliva, and urine) and stool/feces, intestinal content, and tissues for NMR experiments including extraction of polar metabolites and application of NMR in metabolomics studies. One dimensional (1D) 1H NMR experiments with different variations such as pre-saturation, relaxation-edited, and diffusion-edited are routinely acquired for profiling and metabolite identification and quantification. 2D homonuclear 1H-1H TOCSY and COSY, 2D J-resolved, and heteronuclear 1H-13C HSQC and HMBC are also performed to assist with metabolite identification and quantification. The NMR data are then subjected to targeted and/or untargeted multivariate statistical analysis for biomarker discovery, clinical diagnosis, toxicological studies, molecular phenotyping, and functional genomics.
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Affiliation(s)
- Tapas K Mal
- Department of Chemistry, Pennsylvania State University, University Park, PA, USA.
| | - Yuan Tian
- Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
| | - Andrew D Patterson
- Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
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30
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Crook AA, Powers R. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications. Molecules 2020; 25:E5128. [PMID: 33158172 PMCID: PMC7662776 DOI: 10.3390/molecules25215128] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022] Open
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.
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Affiliation(s)
- Alexandra A. Crook
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
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Madrid-Gambin F, Oller-Moreno S, Fernandez L, Bartova S, Giner MP, Joyce C, Ferraro F, Montoliu I, Moco S, Marco S. AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics. Bioinformatics 2020; 36:2943-2945. [PMID: 31930381 DOI: 10.1093/bioinformatics/btaa022] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 12/17/2019] [Accepted: 01/10/2020] [Indexed: 12/22/2022] Open
Abstract
SUMMARY Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. AVAILABILITY AND IMPLEMENTATION The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francisco Madrid-Gambin
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain
| | - Sergio Oller-Moreno
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain
| | - Luis Fernandez
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain.,Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona 08028, Spain
| | - Simona Bartova
- Nestlé Research, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | | | | | | | - Ivan Montoliu
- Nestlé Research, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - Sofia Moco
- Nestlé Research, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - Santiago Marco
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain.,Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona 08028, Spain
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Tian J, Fu G, Xu Z, Chen X, Sun J, Jin B. Urinary exfoliated tumor single-cell metabolomics technology for establishing a drug resistance monitoring system for bladder cancer with intravesical chemotherapy. Med Hypotheses 2020; 143:110100. [DOI: 10.1016/j.mehy.2020.110100] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/06/2020] [Accepted: 07/10/2020] [Indexed: 12/16/2022]
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Cunningham JL, Bramstång L, Singh A, Jayarathna S, Rasmusson AJ, Moazzami A, Müller B. Impact of time and temperature on gut microbiota and SCFA composition in stool samples. PLoS One 2020; 15:e0236944. [PMID: 32745090 PMCID: PMC7398539 DOI: 10.1371/journal.pone.0236944] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/16/2020] [Indexed: 01/08/2023] Open
Abstract
Gut dysbiosis has been implicated in the pathophysiology of a growing number of non-communicable diseases. High through-put sequencing technologies and short chain fatty acid (SCFA) profiling enables surveying of the composition and function of the gut microbiota and provide key insights into host-microbiome interactions. However, a methodological problem with analyzing stool samples is that samples are treated and stored differently prior to submission for analysis potentially influencing the composition of the microbiota and its metabolites. In the present study, we simulated the sample acquisition of a large-scale study, in which stool samples were stored for up to two days in the fridge or at room temperature before being handed over to the hospital. To assess the influence of time and temperature on the microbial community and on SCFA composition in a controlled experimental setting, the stool samples of 10 individuals were exposed to room and fridge temperatures for 24 and 48 hours, respectively, and analyzed using 16S rRNA gene amplicon sequencing, qPCR and nuclear magnetic resonance spectroscopy. To best of our knowledge, this is the first study to investigate the influence of storage time and temperature on the absolute abundance of methanogens, and of Lactobacillus reuteri. The results indicate that values obtained for methanogens, L. reuteri and total bacteria are still representative even after storage for up to 48 hours at RT (20°C) or 4°C. The overall microbial composition and structure appeared to be influenced more by laboratory errors introduced during sample processing than by the actual effects of temperature and time. Although microbial activity was demonstrated by elevated SCFA at both 4°C and RT, SCFAs ratios were more stable over the different conditions and may be considered as long as samples are come from similar storage conditions.
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Affiliation(s)
- Janet L. Cunningham
- Department of Neurosciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Ludvig Bramstång
- Department of Neurosciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Abhijeet Singh
- Department of Molecular Sciences, BioCentrum, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Shishanthi Jayarathna
- Department of Molecular Sciences, BioCentrum, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Annica J. Rasmusson
- Department of Neurosciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Ali Moazzami
- Department of Molecular Sciences, BioCentrum, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Bettina Müller
- Department of Molecular Sciences, BioCentrum, Swedish University of Agricultural Sciences, Uppsala, Sweden
- * E-mail:
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Röhnisch HE, Kyrø C, Olsen A, Thysell E, Hallmans G, Moazzami AA. Identification of metabolites associated with prostate cancer risk: a nested case-control study with long follow-up in the Northern Sweden Health and Disease Study. BMC Med 2020; 18:187. [PMID: 32698845 PMCID: PMC7376662 DOI: 10.1186/s12916-020-01655-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 06/03/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Prostate cancer is the second most frequently diagnosed cancer in men. Metabolomics can potentially provide new insights into the aetiology of prostate cancer by identifying new metabolic risk factors. This study investigated the prospective association between plasma metabolite concentrations and prostate cancer risk, both overall and by stratifying for disease aggressiveness and baseline age. METHODS In a case-control study nested in the Northern Sweden Health and Disease Study, pre-diagnostic concentrations of 148 plasma metabolites were determined using targeted mass spectrometry- and nuclear magnetic resonance-based metabolomics in 777 prostate cancer cases (follow-up ≥ 5 years) and 777 matched controls. Associations between prostate cancer risk and metabolite concentrations were investigated using conditional logistic regression conditioned on matching factors (body mass index, age and sample storage time). Corrections for multiple testing were performed using false discovery rate (20%) and Bonferroni. Metabolomics analyses generated new hypotheses, which were investigated by leveraging food frequency questionnaires (FFQs) and oral glucose tolerance tests performed at baseline. RESULTS After correcting for multiple testing, two lysophosphatidylcholines (LPCs) were positively associated with risk of overall prostate cancer (all ages and in older subjects). The strongest association was for LPC C17:0 in older subjects (OR = 2.08; 95% CI 1.45-2.98; p < 0.0001, significant also after the Bonferroni correction). Observed associations with risk of overall prostate cancer in younger subjects were positive for glycine and inverse for pyruvate. For aggressive prostate cancer, there were positive associations with six glycerophospholipids (LPC C17:0, LPC C20:3, LPC C20:4, PC ae C38:3, PC ae C38:4 and PC ae C40:2), while there was an inverse association with acylcarnitine C18:2. Moreover, plasma LPC C17:0 concentrations positively correlated with estimated dietary intake of fatty acid C17:0 from the FFQs. The associations between glycerophospholipids and prostate cancer were stronger in case-controls with normal glucose tolerance. CONCLUSIONS Several glycerophospholipids were positively associated with risk of overall and aggressive prostate cancer. The strongest association was observed for LPC C17:0. The associations between glycerophospholipids and prostate cancer risk were stronger in case-controls with normal glucose tolerance, suggesting a link between the glucose metabolism status and risk of prostate cancer.
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Affiliation(s)
- Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Box 7015, 75007, Uppsala, Sweden
| | - Cecilie Kyrø
- Unit of Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Anja Olsen
- Unit of Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elin Thysell
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Box 7015, 75007, Uppsala, Sweden.
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35
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MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide. Anal Bioanal Chem 2020; 412:5695-5706. [PMID: 32617759 DOI: 10.1007/s00216-020-02789-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 05/31/2020] [Accepted: 06/24/2020] [Indexed: 10/23/2022]
Abstract
Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of 1H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D 1H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D 1H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses. Graphical abstract.
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Yuan B, Zhang X, Kamal GM, Jiang B, Liu M. Accurate estimation of diffusion coefficient for molecular identification in a complex background. Anal Bioanal Chem 2020; 412:4519-4525. [PMID: 32405677 DOI: 10.1007/s00216-020-02693-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 04/22/2020] [Accepted: 05/04/2020] [Indexed: 11/25/2022]
Abstract
To eliminate the effects of complex background signals and to enhance the accuracy of the diffusion coefficient measurement, derivative NMR spectroscopy with negligible loss of the spectral quality is introduced based on the customized Savitzky-Golay method and used to construct diffusion-ordered NMR spectroscopy (DOSY). The criterion of the method was established by simulations. The application of this method on mouse urine and serum showed that the accuracy and precision of diffusion coefficient measurements in a complex background were improved to enhance the identification of molecules. Graphical abstract Diffusion-ordered NMR spectroscopy is a powerful tool for analyzing complex mixtures. To improve the accuracy of diffusion coefficient measurement, the magnitude of complex derivative spectra is introduced as a post-processing method to eliminate the effects of background signals, broad signals, or distorted baseline. And thus, accurate estimate of the diffusion coefficient is ensured to enhance the molecule identification.
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Affiliation(s)
- Bin Yuan
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China
| | - Xu Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Ghulam Mustafa Kamal
- Department of Chemistry, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan
| | - Bin Jiang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China.
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China.
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
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Signature Mapping (SigMa): An efficient approach for processing complex human urine 1H NMR metabolomics data. Anal Chim Acta 2020; 1108:142-151. [DOI: 10.1016/j.aca.2020.02.025] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 01/26/2020] [Accepted: 02/10/2020] [Indexed: 02/07/2023]
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Abstract
In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.
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Affiliation(s)
- Wimal Pathmasiri
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA.
| | - Kristine Kay
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan McRitchie
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan Sumner
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
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Fredriksson R, Sreedharan S, Nordenankar K, Alsiö J, Lindberg FA, Hutchinson A, Eriksson A, Roshanbin S, Ciuculete DM, Klockars A, Todkar A, Hägglund MG, Hellsten SV, Hindlycke V, Västermark Å, Shevchenko G, Olivo G, K C, Kullander K, Moazzami A, Bergquist J, Olszewski PK, Schiöth HB. The polyamine transporter Slc18b1(VPAT) is important for both short and long time memory and for regulation of polyamine content in the brain. PLoS Genet 2019; 15:e1008455. [PMID: 31800589 PMCID: PMC6927659 DOI: 10.1371/journal.pgen.1008455] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 12/23/2019] [Accepted: 10/03/2019] [Indexed: 01/11/2023] Open
Abstract
SLC18B1 is a sister gene to the vesicular monoamine and acetylcholine transporters, and the only known polyamine transporter, with unknown physiological role. We reveal that Slc18b1 knock out mice has significantly reduced polyamine content in the brain providing the first evidence that Slc18b1 is functionally required for regulating polyamine levels. We found that this mouse has impaired short and long term memory in novel object recognition, radial arm maze and self-administration paradigms. We also show that Slc18b1 KO mice have altered expression of genes involved in Long Term Potentiation, plasticity, calcium signalling and synaptic functions and that expression of components of GABA and glutamate signalling are changed. We further observe a partial resistance to diazepam, manifested as significantly lowered reduction in locomotion after diazepam treatment. We suggest that removal of Slc18b1 leads to reduction of polyamine contents in neurons, resulting in reduced GABA signalling due to long-term reduction in glutamatergic signalling. A fundamental function of the nervous system is its ability to modulate and change the connections between nerve cells, and this forms the basis for memory and learning. This is most well studied for synapses that are using the neurotransmitter glutamate, and a central part of this is referred to Long Term Potentiation. This process is dependent on a specific glutamate receptor called the NMDA receptor, and the function of this receptor can be controlled by various mechanisms. Here, we show that polyamines can regulate this receptor and that lack of polyamines result in impaired learning and memory. Polyamines are small peptides made by many different cells in the body, including cells in the brain, and by removing a gene coding for a transporter important for the release of polyamines in nerve cells of mice, we show that polyamines are important for proper function of the glutamate system. We also show the deletion of this gene result in fundamentally rearranged GABA and glutamate systems, resulting in the mice having a much higher tolerance for the sedative drug benzodiazepines. Polyamines and targets for these molecules could be important points of intervention for future drugs aiming at modulating the glutamatergic system.
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Affiliation(s)
- Robert Fredriksson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- * E-mail:
| | - Smitha Sreedharan
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Karin Nordenankar
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Johan Alsiö
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Frida A. Lindberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Ashley Hutchinson
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Anders Eriksson
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Sahar Roshanbin
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Diana M. Ciuculete
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Anica Klockars
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
- Faculty of Science and Engineering, University of Waikato, Hamilton, New Zealand
| | - Aniruddha Todkar
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Maria G. Hägglund
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Sofie V. Hellsten
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Viktoria Hindlycke
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Åke Västermark
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | | | - Gaia Olivo
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Cheng K
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Klas Kullander
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Ali Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Jonas Bergquist
- Department of Chemistry, Uppsala University, Uppsala, Sweden
| | - Pawel K. Olszewski
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
- Faculty of Science and Engineering, University of Waikato, Hamilton, New Zealand
| | - Helgi B. Schiöth
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
- Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
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Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev 2019; 99:1819-1875. [PMID: 31434538 DOI: 10.1152/physrev.00035.2018] [Citation(s) in RCA: 464] [Impact Index Per Article: 92.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.
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Affiliation(s)
- David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
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Khalili B, Tomasoni M, Mattei M, Mallol Parera R, Sonmez R, Krefl D, Rueedi R, Bergmann S. Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites. J Proteome Res 2019; 18:3360-3368. [PMID: 31318216 DOI: 10.1021/acs.jproteome.9b00295] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.
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Affiliation(s)
- Bita Khalili
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Mattia Tomasoni
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Mirjam Mattei
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Roger Mallol Parera
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Reyhan Sonmez
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Daniel Krefl
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Rico Rueedi
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Sven Bergmann
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.,Department of Integrative Biomedical Sciences , University of Cape Town , Cape Town 7700 , South Africa
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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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Emwas AH, Roy R, McKay RT, Tenori L, Saccenti E, Gowda GAN, Raftery D, Alahmari F, Jaremko L, Jaremko M, Wishart DS. NMR Spectroscopy for Metabolomics Research. Metabolites 2019; 9:E123. [PMID: 31252628 PMCID: PMC6680826 DOI: 10.3390/metabo9070123] [Citation(s) in RCA: 490] [Impact Index Per Article: 98.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/14/2019] [Accepted: 06/18/2019] [Indexed: 12/14/2022] Open
Abstract
Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.
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Affiliation(s)
- Abdul-Hamid Emwas
- Core Labs, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Raja Roy
- Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Uttar Pradesh 226014, India
| | - Ryan T McKay
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2W2, Canada
| | - Leonardo Tenori
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
- Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue, Seattle, WA 98109, USA
| | - Fatimah Alahmari
- Department of NanoMedicine Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia
| | - Lukasz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Mariusz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
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Jensen HM, Bertram HC. The magic angle view to food: magic-angle spinning (MAS) NMR spectroscopy in food science. Metabolomics 2019; 15:44. [PMID: 30868337 DOI: 10.1007/s11306-019-1504-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/04/2019] [Indexed: 01/16/2023]
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy has been used in food science and nutritional studies for decades and is one of the major analytical platforms in metabolomics. Many foods are solid or at least semi-solid, which denotes that the molecular motions are restricted as opposed to in pure liquids. While the majority of NMR spectroscopy is performed on liquid samples and a solid material gives rise to constraints in terms of many chemical analyses, the magic angle thrillingly enables the application of NMR spectroscopy also on semi-solid and solid materials. This paper attempts to review how magic-angle spinning (MAS) NMR is used from 'farm-to-fork' in food science.
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Affiliation(s)
- Henrik Max Jensen
- DuPont Nutrition Biosciences ApS, Edwin Rahrsvej 38, 8220, Brabrand, Denmark
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Söder J, Höglund K, Dicksved J, Hagman R, Eriksson Röhnisch H, Moazzami AA, Wernersson S. Plasma metabolomics reveals lower carnitine concentrations in overweight Labrador Retriever dogs. Acta Vet Scand 2019; 61:10. [PMID: 30808390 PMCID: PMC6390349 DOI: 10.1186/s13028-019-0446-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 02/18/2019] [Indexed: 12/20/2022] Open
Abstract
Background The prevalence of overweight is increasing in dogs, but the metabolic events related to this condition are still poorly understood. The purpose of the study was to investigate the postprandial response of plasma metabolites using a meal-challenge test and to identify metabolic variations related to spontaneous overweightness in privately owned dogs. Results Twenty-eight healthy male intact Labrador Retriever dogs were included, 12 of which were classified as lean (body condition score (BCS) 4–5 on a 9-point scale) and 16 as overweight (BCS 6–8). After an overnight fast (14–17 h), blood samples were collected and dogs were thereafter fed a high-fat meal. Postprandial blood samples were collected hourly four times. Plasma metabolites were identified by nuclear magnetic resonance. Postprandial metabolomes differed from the fasting metabolome in multivariate discriminant analysis (PLS-DA: Q2Y = 0.31–0.63, cross-validated ANOVA: P ≤ 0.00014) Eleven metabolites, all amino acids, contributed to the separations. Carnitine was identified as a metabolite related to overweight (stepwise logistic regression analysis P ≤ 0.03) and overweight dogs had overall lower carnitine response (mixed model repeated measures analysis P = 0.005) than lean dogs. Notably, mean fasting carnitine concentration in overweight dogs (9.4 ± 4.2 µM) was close to a proposed reference limit for carnitine insufficiency. Conclusions A postprandial amino acid response was detected but no time-dependent variations with regards to body condition groups were found. Lower carnitine concentrations were found in overweight compared to lean dogs. The latter finding could indicate a carnitine insufficiency related to spontaneous adiposity and altered lipid metabolism in overweight dogs in this cohort of otherwise healthy Labrador Retrievers. Electronic supplementary material The online version of this article (10.1186/s13028-019-0446-4) contains supplementary material, which is available to authorized users.
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Joesten WC, Kennedy MA. RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies. Metabolomics 2019; 15:5. [PMID: 30830432 DOI: 10.1007/s11306-018-1465-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 12/20/2018] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data. OBJECTIVES To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies. METHODS The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment. RESULTS Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories. CONCLUSION RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.
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Affiliation(s)
- William C Joesten
- Department of Chemistry and Biochemistry, Miami University, 106 Hughes Laboratories, Oxford, OH, 45056, USA
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, 106 Hughes Laboratories, Oxford, OH, 45056, USA.
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Lipfert M, Rout MK, Berjanskii M, Wishart DS. Automated Tools for the Analysis of 1D-NMR and 2D-NMR Spectra. Methods Mol Biol 2019; 2037:429-449. [PMID: 31463859 DOI: 10.1007/978-1-4939-9690-2_24] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is becoming increasingly automated. Most modern NMR spectrometers are now equipped with auto-tune/auto-match probes along with automated locking and shimming systems. Likewise, more and more instruments, especially for NMR-based metabolomics applications, are equipped with automated sample changers. All this instrumental automation allows NMR data to be collected at a rate of >100 samples/day. However, a continuing bottleneck in NMR-based metabolomics has been the time required to manually analyze and annotate the collected NMR spectra. In many cases, manual spectral annotation and analysis can take one or more hours per spectrum. Fortunately, over the past few years, several software tools have been developed that largely automate the spectral deconvolution or spectral annotation process. Using these tools requires that the samples must be prepared and the NMR spectra must be acquired in a very specific manner. In this chapter, we will describe the step-by-step preparation of biofluid samples along with the required protocols for acquiring optimal spectra for automated NMR metabolomics analysis. We will also discuss the use of three common tools (Chenomx NMR Suite, Bayesil, and COLMARm) for (semi-) automated profiling, and annotation of 1D- and 2D-NMR spectra of biofluids.
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Affiliation(s)
- Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Manoj Kumar Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
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Misra BB, Mohapatra S. Tools and resources for metabolomics research community: A 2017-2018 update. Electrophoresis 2018; 40:227-246. [PMID: 30443919 DOI: 10.1002/elps.201800428] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/09/2018] [Accepted: 11/09/2018] [Indexed: 01/09/2023]
Abstract
The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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Noorbakhsh H, Yavarmanesh M, Mortazavi SA, Adibi P, Moazzami AA. Metabolomics analysis revealed metabolic changes in patients with diarrhea-predominant irritable bowel syndrome and metabolic responses to a synbiotic yogurt intervention. Eur J Nutr 2018; 58:3109-3119. [PMID: 30392136 DOI: 10.1007/s00394-018-1855-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 10/29/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE Irritable bowel syndrome is a gastrointestinal disorder which can influence human metabolism. It has been demonstrated that probiotics are beneficial in controlling IBS. Thus, the main objective of the present study was to determine metabolic changes in response to diarrhea predominant irritable bowel syndrome (IBS-D) and to investigate the metabolic effects of a synbiotic intervention on serum, urine, and stool samples from IBS-D patients and healthy controls using proton nuclear magnetic resonance (1HNMR). METHODS A 1HNMR-based metabolomics study was conducted on urine and serum metabolites from 16 healthy and eight IBS-D participants at baseline and after 4 weeks of a synbiotic yogurt intervention. RESULTS At the baseline, serum acetoacetate, myo-inositol, and sarcosine concentrations were higher and threonine and methionine concentrations were lower in the IBS-D cohort than the control group. Moreover, Indoxyl-sulfate concentration of urine was lower and dimethylamine and taurine were higher in the IBS-D group. After intervention, serum concentration of ketone bodies decreased, choline, phenylalanine, and branched-chain amino acids increased in IBS-D group. Metabolomics analysis indicated a shift in one-carbon metabolism. Thus, the level of serum homocysteine was determined and found to be higher in the IBS-D cohort at baseline, and then decreased after the intervention. CONCLUSION IBS causes a shift in one-carbon metabolism and these changes can be reversed by a synbiotic intervention. An increase in the number of fecal Lactobacilli and an improvement in the health status of IBS-D patients were also observed in response to intervention.
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Affiliation(s)
- Hamid Noorbakhsh
- Department of Food Science and Technology, Collage of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Masoud Yavarmanesh
- Department of Food Science and Technology, Collage of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Seyed Ali Mortazavi
- Department of Food Science and Technology, Collage of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Peyman Adibi
- Integrative Functional Gastroenterology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
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Kennedy AD, Wittmann BM, Evans AM, Miller LAD, Toal DR, Lonergan S, Elsea SH, Pappan KL. Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing. JOURNAL OF MASS SPECTROMETRY : JMS 2018; 53:1143-1154. [PMID: 30242936 DOI: 10.1002/jms.4292] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/10/2018] [Accepted: 09/17/2018] [Indexed: 06/08/2023]
Abstract
Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.
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Affiliation(s)
| | | | | | | | | | | | - Sarah H Elsea
- Department of Molecular and Human Genetics and Baylor Genetics, Baylor College of Medicine, Houston, TX, USA
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