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Ovbude ST, Sharmeen S, Kyei I, Olupathage H, Jones J, Bell RJ, Powers R, Hage DS. Applications of chromatographic methods in metabolomics: A review. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1239:124124. [PMID: 38640794 DOI: 10.1016/j.jchromb.2024.124124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/11/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.
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Affiliation(s)
- Susan T Ovbude
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Sadia Sharmeen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Isaac Kyei
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Harshana Olupathage
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Jacob Jones
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Richard J Bell
- 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
| | - David S Hage
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA.
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Wu Y, Sanati O, Uchimiya M, Krishnamurthy K, Wedell J, Hoch JC, Edison AS, Delaglio F. SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification. Anal Chem 2024; 96:1843-1851. [PMID: 38273718 PMCID: PMC10896553 DOI: 10.1021/acs.analchem.3c03078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 01/27/2024]
Abstract
Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.
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Affiliation(s)
- Yue Wu
- Institute
of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Omid Sanati
- School
of Electrical and Computer Engineering, University of Georgia, Athens, Georgia 30602, United States
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Mario Uchimiya
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | | | - Jonathan Wedell
- National
Magnetic Resonance Facility, University
of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Jeffrey C. Hoch
- Department
of Molecular Biology and Biophysics, University
of Connecticut, Farmington, Connecticut 06030-3305, United States
| | - Arthur S. Edison
- Institute
of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
- Department
of Biochemistry and Molecular Biology, University
of Georgia, Athens, Georgia 30602, United States
| | - Frank Delaglio
- Institute
for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University
of Maryland, Rockville, Maryland 20850, United States
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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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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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Han X, Wang W, Ma LH, AI-Ramahi I, Botas J, MacKenzie K, Allen GI, Young DW, Liu Z, Maletic-Savatic M. SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra. Bioinformatics 2023; 39:btad593. [PMID: 37792497 PMCID: PMC10568371 DOI: 10.1093/bioinformatics/btad593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 07/31/2023] [Accepted: 10/02/2023] [Indexed: 10/06/2023] Open
Abstract
MOTIVATION Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. RESULTS As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. AVAILABILITY AND IMPLEMENTATION The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.
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Affiliation(s)
- Xu Han
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Wanli Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Graduate Program of Quantitative & Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, United States
| | - Li-Hua Ma
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, United States
| | - Ismael AI-Ramahi
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Juan Botas
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Kevin MacKenzie
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, United States
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, United States
| | - Genevera I Allen
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Electrical and Computer Engineering, Statistics, and Computer Science, Rice University, Houston, TX 77005-1827, United States
| | - Damian W Young
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, United States
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Mirjana Maletic-Savatic
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States
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6
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Canlet C, Deborde C, Cahoreau E, Da Costa G, Gautier R, Jacob D, Jousse C, Lacaze M, Le Mao I, Martineau E, Peyriga L, Richard T, Silvestre V, Traïkia M, Moing A, Giraudeau P. NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows. Metabolomics 2023; 19:65. [PMID: 37418094 DOI: 10.1007/s11306-023-02028-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
INTRODUCTION Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling. OBJECTIVES An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision. METHODS A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool. RESULTS For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables. CONCLUSION External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.
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Affiliation(s)
- Cécile Canlet
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Catherine Deborde
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France
| | - Edern Cahoreau
- TBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Grégory Da Costa
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | - Roselyne Gautier
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Daniel Jacob
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France
| | - Cyril Jousse
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Mélia Lacaze
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Inès Le Mao
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | - Estelle Martineau
- Nantes Université, CNRS, CEISAM UMR 6230, 44000, Nantes, France
- CAPACITES SAS, 44200, Nantes, France
| | - Lindsay Peyriga
- TBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Tristan Richard
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | | | - Mounir Traïkia
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Annick Moing
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France.
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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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8
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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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9
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Wang T, Wang XW, Lee-Sarwar KA, Litonjua AA, Weiss ST, Sun Y, Maslov S, Liu YY. Predicting metabolomic profiles from microbial composition through neural ordinary differential equations. NAT MACH INTELL 2023; 5:284-293. [PMID: 38223254 PMCID: PMC10786629 DOI: 10.1038/s42256-023-00627-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 02/03/2023] [Indexed: 03/14/2023]
Abstract
Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.
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Affiliation(s)
- Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kathleen A. Lee-Sarwar
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Allergy and Clinical Immunology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Augusto A. Litonjua
- Pediatric Pulmonology, Golisano Children’s Hospital, University of Rochester, Rochester, NY 14642, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yizhou Sun
- Department of Computer Science, University of California, Los Angeles, USA
| | - Sergei Maslov
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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10
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Han X, Wang W, Ma LH, Al-Ramahi I, Botas J, MacKenzie K, Allen GI, Young DW, Liu Z, Maletic-Savatic M. SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529564. [PMID: 36865102 PMCID: PMC9980041 DOI: 10.1101/2023.02.22.529564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.
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Affiliation(s)
- Xu Han
- 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
| | - 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
| | - Ismael Al-Ramahi
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Juan Botas
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kevin MacKenzie
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
- Center for Drug Discovery, Department of Pathology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Genevera I. Allen
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005-1827, USA
| | - Damian W. Young
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, 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
| | - 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
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11
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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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12
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Tang F, Krishnamurthy K, Janovick J, Crawford L, Wang S, Hatzakis E. Advancing NMR-based metabolomics using Complete Reduction to Amplitude Frequency Table: Cultivar differentiation of black ripe table olives as a case study. Food Chem 2022; 405:134868. [DOI: 10.1016/j.foodchem.2022.134868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/24/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022]
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13
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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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14
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Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR). Comput Struct Biotechnol J 2021; 19:5047-5058. [PMID: 34589182 PMCID: PMC8455648 DOI: 10.1016/j.csbj.2021.08.048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/29/2021] [Accepted: 08/30/2021] [Indexed: 12/20/2022] Open
Abstract
Automatic assignment of metabolites of 2D-TOCSY NMR spectra. Semi-supervised learning for metabolic profiling. Deconvolution and metabolic profiling of 2D NMR spectra using Machine Learning. Accurate Automatic multicomponent assignment of 2D NMR spectrum.
Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from 1H-1H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the 1H–1H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology.
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15
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Bhushan B, Upadhyay D, Sharma U, Jagannathan N, Singh SB, Ganju L. Urine metabolite profiling of Indian Antarctic Expedition members: NMR spectroscopy-based metabolomic investigation. Heliyon 2021; 7:e07114. [PMID: 34113732 PMCID: PMC8170161 DOI: 10.1016/j.heliyon.2021.e07114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 04/12/2021] [Accepted: 05/17/2021] [Indexed: 12/20/2022] Open
Abstract
The southernmost region of earth, Antarctica, has world's most challenging environments. Those who live for long time and work in Antarctic stations are subjected to environmental stresses such as cold weather, photoperiod variations leading to disrupted sleep cycles, constrained living spaces, dry air, non-availability of fresh food items, and high electromagnetic radiations, psychological factors, such as geographical and social isolation, etc. All these factors have a significant impact on the human body. The present study investigated the impact of Antarctica harsh environment on human physiology and its metabolic processes by evaluating urine metabolome, using 1H NMR spectroscopy and analyzing certain physiological and clinical parameters for correlation with physiological expression data and metabolite results. Two study groups - before Antarctic exposure (B) and after Antarctic exposure (E), consisting of 11 subjects, exposed to one-month summer expedition, were compared. 35 metabolites in urine samples were identified from the 700 MHz 1H NMR spectra from where integral intensity of 22 important metabolites was determined. Univariate analysis indicated significant decrease in the levels of citrate and creatinine in samples collected post-expedition. Multivariate analysis was also performed using 1H NMR spectroscopy, because independent metabolite abundances may complement each other in predicting the dependent variables. 10 metabolites were identified among the groups; the OPLS-DA and VIP score indicated variation in appearance of metabolites over different time periods with insignificant change in the intensities. Metabolite results illustrate the impact of environmental stress or altered life style including the diet with absence of fresh fruits and vegetables, on the pathophysiology of the human health. Metabolic adaptation to Antarctic environmental stressors may help to highlight the effect of short-term physiological status and provide important information during Antarctic expeditions to formulate management programmes.
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Affiliation(s)
- Brij Bhushan
- Defence Institute of Physiology and Allied Sciences (DIPAS), DRDO, Lucknow Road, Timarpur, New Delhi 110054, India
| | - Deepti Upadhyay
- All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - Uma Sharma
- All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | | | - Shashi Bala Singh
- National Institute of Pharmaceutical Education and Research (NIPER), NH 9, Kukatpally Industrial Estate, Balanagar, Hyderabad, Telangana 500037, India
| | - Lilly Ganju
- Defence Institute of Physiology and Allied Sciences (DIPAS), DRDO, Lucknow Road, Timarpur, New Delhi 110054, India
- Corresponding author.
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16
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Mach N, Moroldo M, Rau A, Lecardonnel J, Le Moyec L, Robert C, Barrey E. Understanding the Holobiont: Crosstalk Between Gut Microbiota and Mitochondria During Long Exercise in Horse. Front Mol Biosci 2021; 8:656204. [PMID: 33898524 PMCID: PMC8063112 DOI: 10.3389/fmolb.2021.656204] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 02/16/2021] [Indexed: 12/17/2022] Open
Abstract
Endurance exercise has a dramatic impact on the functionality of mitochondria and on the composition of the intestinal microbiome, but the mechanisms regulating the crosstalk between these two components are still largely unknown. Here, we sampled 20 elite horses before and after an endurance race and used blood transcriptome, blood metabolome and fecal microbiome to describe the gut-mitochondria crosstalk. A subset of mitochondria-related differentially expressed genes involved in pathways such as energy metabolism, oxidative stress and inflammation was discovered and then shown to be associated with butyrate-producing bacteria of the Lachnospiraceae family, especially Eubacterium. The mechanisms involved were not fully understood, but through the action of their metabolites likely acted on PPARγ, the FRX-CREB axis and their downstream targets to delay the onset of hypoglycemia, inflammation and extend running time. Our results also suggested that circulating free fatty acids may act not merely as fuel but drive mitochondrial inflammatory responses triggered by the translocation of gut bacterial polysaccharides following endurance. Targeting the gut-mitochondria axis therefore appears to be a potential strategy to enhance athletic performance.
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Affiliation(s)
- Núria Mach
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Marco Moroldo
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Andrea Rau
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.,BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, Estrées-Mons, France
| | - Jérôme Lecardonnel
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Laurence Le Moyec
- Université d'Évry Val d'Essonne, Université Paris-Saclay, Évry, France ABI UMR 1313, INRAE, Université Paris-Saclay, AgroParisTech, Jouy-en-Josas, France.,MCAM UMR7245, CNRS, Muséum National d'Histoire Naturelle, Paris, France
| | - Céline Robert
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.,École Nationale Vétérinaire d'Alfort, Maisons-Alfort, France
| | - Eric Barrey
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
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17
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Nemadodzi LE, Vervoort J, Prinsloo G. NMR-Based Metabolomic Analysis and Microbial Composition of Soil Supporting Burkea africana Growth. Metabolites 2020; 10:E402. [PMID: 33050369 PMCID: PMC7600111 DOI: 10.3390/metabo10100402] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 09/14/2020] [Accepted: 09/21/2020] [Indexed: 11/17/2022] Open
Abstract
Burkea africana is a leguminous tree used for medicinal purposes, growing in clusters, on soils impoverished from most nutrients. The study aimed to determine the factors responsible for successful reproduction and establishment of the B. africana trees in nature, as all efforts for commercial production has been proven unsuccessful. An investigation was carried out to determine the metabolomic profile, chemical composition, and microbial composition of the soils where B. africana grows (Burkea soil) versus the soil where it does not grow (non-Burkea soil). 1H-NMR metabolomic analysis showed different metabolites in the respective soils. Trehalose and betaine, as well as a choline-like and carnitine-like compound, were found to be in higher concentration in Burkea soils, whereas, acetate, lactate, and formate were concentrated in non-Burkea soils. Liquid Chromatography-Mass Spectrometry analysis revealed the presence of numerous amino acids such as aspartic acid and glutamine to be higher in Burkea soils. Since it was previously suggested that the soil microbial diversity is the major driver for establishment and survival of seedlings in nature, Deoxyribonucleic acid (DNA) was extracted and a BLAST analysis conducted for species identification. Penicillium species was found to be highly prevalent and discriminant between the two soils, associated with the Burkea soils. No differences in the bacterial composition of Burkea and non-Burkea soils were observed. The variances in fungal composition suggests that species supremacy play a role in development of B. africana trees and is responsible for creating a supporting environment for natural establishment and survival of seedlings.
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Affiliation(s)
- Lufuno Ethel Nemadodzi
- Department of Agriculture and Animal Health, University of South Africa, Science Campus, Florida, Johannesburg 1710, South Africa;
- ABBERU, University of South Africa, Science Campus, Florida, Johannesburg 1710, South Africa
| | - Jacques Vervoort
- Laboratory of Biochemistry, Wageningen University, 6708 PB Wageningen, The Netherlands;
| | - Gerhard Prinsloo
- Department of Agriculture and Animal Health, University of South Africa, Science Campus, Florida, Johannesburg 1710, South Africa;
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18
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Hajjar G, Merchak N, Daniel C, Rizk T, Akoka S, Bejjani J. Improved lipid mixtures profiling by 1H NMR using reference lineshape adjustment and deconvolution techniques. Talanta 2020; 208:120475. [DOI: 10.1016/j.talanta.2019.120475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/19/2019] [Accepted: 10/13/2019] [Indexed: 10/25/2022]
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19
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Aliakbari A, Ehsani A, Vaez Torshizi R, Løvendahl P, Esfandyari H, Jensen J, Sarup P. Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1. J Anim Sci 2019; 97:3832-3844. [PMID: 31278866 PMCID: PMC6735847 DOI: 10.1093/jas/skz228] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 07/04/2019] [Indexed: 11/14/2022] Open
Abstract
In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154-294), 294 to 336 d of age (ADG294-336), and 154 to 336 d of age (ADG154-336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154-294, ADG294-336, and ADG154-336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.
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Affiliation(s)
- Amir Aliakbari
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Alireza Ehsani
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Rasoul Vaez Torshizi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Peter Løvendahl
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Hadi Esfandyari
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Just Jensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
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20
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Plancade S, Clark A, Philippe C, Helbling JC, Moisan MP, Esquerré D, Le Moyec L, Robert C, Barrey E, Mach N. Unraveling the effects of the gut microbiota composition and function on horse endurance physiology. Sci Rep 2019; 9:9620. [PMID: 31270376 PMCID: PMC6610142 DOI: 10.1038/s41598-019-46118-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 06/20/2019] [Indexed: 12/12/2022] Open
Abstract
An integrated analysis of gut microbiota, blood biochemical and metabolome in 52 endurance horses was performed. Clustering by gut microbiota revealed the existence of two communities mainly driven by diet as host properties showed little effect. Community 1 presented lower richness and diversity, but higher dominance and rarity of species, including some pathobionts. Moreover, its microbiota composition was tightly linked to host blood metabolites related to lipid metabolism and glycolysis at basal time. Despite the lower fiber intake, community type 1 appeared more specialized to produce acetate as a mean of maintaining the energy supply as glucose concentrations fell during the race. On the other hand, community type 2 showed an enrichment of fibrolytic and cellulolytic bacteria as well as anaerobic fungi, coupled to a higher production of propionate and butyrate. The higher butyrate proportion in community 2 was not associated with protective effects on telomere lengths but could have ameliorated mucosal inflammation and oxidative status. The gut microbiota was neither associated with the blood biochemical markers nor metabolome during the endurance race, and did not provide a biomarker for race ranking or risk of failure to finish the race.
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Affiliation(s)
- Sandra Plancade
- MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, France
- ISBA, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Allison Clark
- Gastroenterology Department, Vall d'Hebron Institut de Reserca, Barcelona, Spain
| | - Catherine Philippe
- UMR 1319, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | | | - Marie-Pierre Moisan
- UMR 1286, INRA, Université Bordeaux, Nutrition et neurobiologie intégrée, Bordeaux, France
| | | | - Laurence Le Moyec
- Unité de Biologie Intégrative et Adaptation à l'Exercice, UBIAE, EA7362, Université d'Evry, Université Paris-Saclay, Evry, France
| | - Céline Robert
- UMR 1313, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d'Alfort, Maisons-Alfort, France
| | - Eric Barrey
- UMR 1313, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Núria Mach
- UMR 1313, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
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21
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Tynkkynen T, Wang Q, Ekholm J, Anufrieva O, Ohukainen P, Vepsäläinen J, Männikkö M, Keinänen-Kiukaanniemi S, Holmes MV, Goodwin M, Ring S, Chambers JC, Kooner J, Järvelin MR, Kettunen J, Hill M, Davey Smith G, Ala-Korpela M. Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics. Int J Epidemiol 2019; 48:978-993. [PMID: 30689875 PMCID: PMC6659374 DOI: 10.1093/ije/dyy287] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. METHODS We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). RESULTS Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. CONCLUSION Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.
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Affiliation(s)
- Tuulia Tynkkynen
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
| | - Qin Wang
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Jussi Ekholm
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Olga Anufrieva
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Pauli Ohukainen
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Jouko Vepsäläinen
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Minna Männikkö
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Sirkka Keinänen-Kiukaanniemi
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Michael V Holmes
- Medical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Matthew Goodwin
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, University of Bristol, Bristol, UK
| | - Susan Ring
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, University of Bristol, Bristol, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Ealing Hospital NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Jaspal Kooner
- Ealing Hospital NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- THL: National Institute for Health and Welfare, Helsinki, Finland
| | - Michael Hill
- Medical Research Council Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, University of Bristol, Bristol, UK
| | - Mika Ala-Korpela
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, University of Bristol, Bristol, UK
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Alfred Hospital, Monash University, Melbourne, VIC, Australia
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22
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Matviychuk Y, Yeo J, Holland DJ. A field-invariant method for quantitative analysis with benchtop NMR. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 298:35-47. [PMID: 30529048 DOI: 10.1016/j.jmr.2018.11.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 11/26/2018] [Accepted: 11/26/2018] [Indexed: 06/09/2023]
Abstract
Recently developed benchtop instruments have the potential of bringing the benefits of NMR spectroscopy to the wide variety of industrial applications. Unfortunately, their low spectral resolution poses significant challenges for traditional quantification approach. Here we present a novel model-based method designed to overcome these challenges. By defining our models in terms of quantum mechanical properties of the underlying spin system, we make our approach invariant to the spectrometer field strength and especially suitable for analyzing benchtop data. Our experimental results on prepared samples and natural fruit juices confirm the applicability of our method for quantitative analysis of medium-field 1H NMR spectra. The developed method succeeds in accurately separating the spectra of glucose anomers and even monitoring their interconversion in non-deuterated water. Furthermore, the compositions of unbuffered natural fruit juices estimated using data from 43 MHz to 400 MHz spectrometers are in good agreement with each other and with the reference values from nutrition databases.
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Affiliation(s)
- Yevgen Matviychuk
- University of Canterbury, Private Bag 4800, Cristchurch 8140, New Zealand
| | - Jet Yeo
- University of Canterbury, Private Bag 4800, Cristchurch 8140, New Zealand
| | - Daniel J Holland
- University of Canterbury, Private Bag 4800, Cristchurch 8140, New Zealand.
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23
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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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24
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Sokolenko S, Jézéquel T, Hajjar G, Farjon J, Akoka S, Giraudeau P. Robust 1D NMR lineshape fitting using real and imaginary data in the frequency domain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 298:91-100. [PMID: 30530098 DOI: 10.1016/j.jmr.2018.11.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/16/2018] [Accepted: 11/20/2018] [Indexed: 06/09/2023]
Abstract
Quantitative NMR is intrinsically dependent on precise, accurate, and robust peak area calculation. In this work, we demonstrate how the use of complex-valued peak descriptions can improve peak fitting in the frequency domain - incorporating phase and baseline correction as well as apodization while working with commonly used Fourier-transformed data. The method has been implemented in an open source R package called rnmrfit that is available for download on GitHub (https://github.com/ssokolen/rnmrfit). Application to real data suggests that this approach can also result in dramatically higher precision than can be achieved with existing software. Simulation data indicates that coefficients of variation below 0.1% can be readily achieved at signal to noise (SNR) ratios of approximately 100. The use of complex-valued data in the frequency domain is demonstrated as a relatively simple and effective means of improving peak fitting for quantitative NMR analysis.
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Affiliation(s)
- Stanislav Sokolenko
- Department of Process Engineering and Applied Science, Dalhousie University, 1360 Barrington St., PO Box 15000, Halifax, NS B3H 4R2, Canada.
| | - Tangi Jézéquel
- CEISAM, UMR CNRS 6230, Bât. 22 Faculté des Sciences et Techniques 2 rue de la Houssinière, 44322 Nantes Cedex 03, France
| | - Ghina Hajjar
- CEISAM, UMR CNRS 6230, Bât. 22 Faculté des Sciences et Techniques 2 rue de la Houssinière, 44322 Nantes Cedex 03, France; Laboratory of Metrology and Isotopic Fractionation, Research Unit: Technologies et Valorisation Agroalimentaire (TVA), Faculty of Science, Saint-Joseph University of Beirut, PO Box 17-5208 Mar Mikhael, Beirut 1104 2020, Lebanon
| | - Jonathan Farjon
- CEISAM, UMR CNRS 6230, Bât. 22 Faculté des Sciences et Techniques 2 rue de la Houssinière, 44322 Nantes Cedex 03, France
| | - Serge Akoka
- CEISAM, UMR CNRS 6230, Bât. 22 Faculté des Sciences et Techniques 2 rue de la Houssinière, 44322 Nantes Cedex 03, France
| | - Patrick Giraudeau
- CEISAM, UMR CNRS 6230, Bât. 22 Faculté des Sciences et Techniques 2 rue de la Houssinière, 44322 Nantes Cedex 03, France; Institut Universitaire de France, 1 rue Descartes, 75005 Paris Cedex 05, France
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25
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Emwas AH, Saccenti E, Gao X, McKay RT, dos Santos VAPM, Roy R, Wishart DS. Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine. Metabolomics 2018; 14:31. [PMID: 29479299 PMCID: PMC5809546 DOI: 10.1007/s11306-018-1321-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 01/09/2018] [Indexed: 12/11/2022]
Abstract
1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.
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Affiliation(s)
- Abdul-Hamid Emwas
- Imaging and Characterization Core Lab, KAUST, Thuwal, 23955-6900 Kingdom of Saudi Arabia
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955 Kingdom of Saudi Arabia
| | - Ryan T. McKay
- Department of Chemistry, University of Alberta, Edmonton, Canada
| | - Vitor A. P. Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Raja Roy
- Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Lucknow, India
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
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26
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Ebbels TMD, Rodriguez-Martinez A, Dumas ME, Keun HC. Advances in Computational Analysis of Metabolomic NMR Data. NMR-BASED METABOLOMICS 2018. [DOI: 10.1039/9781782627937-00310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.
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Affiliation(s)
- Timothy M. D. Ebbels
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Andrea Rodriguez-Martinez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Marc-Emmanuel Dumas
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Hector C. Keun
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London W12 0NN UK
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27
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Röhnisch HE, Eriksson J, Müllner E, Agback P, Sandström C, Moazzami AA. AQuA: An Automated Quantification Algorithm for High-Throughput NMR-Based Metabolomics and Its Application in Human Plasma. Anal Chem 2018; 90:2095-2102. [PMID: 29260864 DOI: 10.1021/acs.analchem.7b04324] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A key limiting step for high-throughput NMR-based metabolomics is the lack of rapid and accurate tools for absolute quantification of many metabolites. We developed, implemented, and evaluated an algorithm, AQuA (Automated Quantification Algorithm), for targeted metabolite quantification from complex 1H NMR spectra. AQuA operates based on spectral data extracted from a library consisting of one standard calibration spectrum for each metabolite. It uses one preselected NMR signal per metabolite for determining absolute concentrations and does so by effectively accounting for interferences caused by other metabolites. AQuA was implemented and evaluated using experimental NMR spectra from human plasma. The accuracy of AQuA was tested and confirmed in comparison with a manual spectral fitting approach using the ChenomX software, in which 61 out of 67 metabolites quantified in 30 human plasma spectra showed a goodness-of-fit (r2) close to or exceeding 0.9 between the two approaches. In addition, three quality indicators generated by AQuA, namely, occurrence, interference, and positional deviation, were studied. These quality indicators permit evaluation of the results each time the algorithm is operated. The efficiency was tested and confirmed by implementing AQuA for quantification of 67 metabolites in a large data set comprising 1342 experimental spectra from human plasma, in which the whole computation took less than 1 s.
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Affiliation(s)
- Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences , Uppsala, Sweden 75651
| | - Jan Eriksson
- Department of Molecular Sciences, Swedish University of Agricultural Sciences , Uppsala, Sweden 75651
| | - Elisabeth Müllner
- Department of Molecular Sciences, Swedish University of Agricultural Sciences , Uppsala, Sweden 75651
| | - Peter Agback
- Department of Molecular Sciences, Swedish University of Agricultural Sciences , Uppsala, Sweden 75651
| | - Corine Sandström
- Department of Molecular Sciences, Swedish University of Agricultural Sciences , Uppsala, Sweden 75651
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences , Uppsala, Sweden 75651
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28
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Abstract
Fecal analysis can generate data that is relevant for the exploration of gut microbiota and their relationship with the host. Nuclear magnetic resonance (NMR) spectroscopy is an excellent tool for the profiling of fecal extracts as it enables the simultaneous detection of various metabolites from a broad range of chemical classes including, among others, short-chain fatty acids, organic acids, amino acids, bile acids, carbohydrates, amines, and alcohols. Compounds present at low μM concentrations can be detected and quantified with a single measurement. Moreover, NMR-based profiling requires a relatively simple sample preparation. Here we describe the three main steps of the general workflow for the NMR-based profiling of feces: sample preparation, NMR data acquisition, and data analysis.
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Affiliation(s)
- Hye Kyong Kim
- Natural Product Laboratory, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Sarantos Kostidis
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Young Hae Choi
- Natural Product Laboratory, Institute of Biology, Leiden University, Leiden, The Netherlands.
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29
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Takis PG, Schäfer H, Spraul M, Luchinat C. Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool. Nat Commun 2017; 8:1662. [PMID: 29162796 PMCID: PMC5698486 DOI: 10.1038/s41467-017-01587-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 09/29/2017] [Indexed: 02/08/2023] Open
Abstract
The NMR chemical shifts of a substance in a complex mixture strongly depend on the composition of the mixture itself, as many weak interactions occur that are hardly predictable. Chemical shift variability is the major obstacle to automatically assigning, and subsequently quantitating, metabolite signals in body fluids, particularly urine. Here we demonstrate that the chemical shifts of signals in urine are actually predictable. This is achieved by constructing ca. 4000 artificial mixtures where the concentrations of 52 most abundant urine metabolites-including 11 inorganic ions-are varied, to sparsely but efficiently populate an N-dimensional concentration matrix. A strong relationship is established between the concentration matrix and the chemical shift matrix, so that chemical shifts of > 90 metabolite signals can be accurately predicted in real urine samples. The concentrations of the invisible inorganic ions are also accurately predicted, along with those of albumin and of several other abundant urine components.
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Affiliation(s)
- Panteleimon G Takis
- Giotto Biotech S.R.L., Via Madonna del Piano 6, 50019, Sesto Fiorentino (FI), Italy
| | - Hartmut Schäfer
- Bruker BioSpin, Silberstreifen, D-76287, Rheinstetten, Germany
| | - Manfred Spraul
- Bruker BioSpin, Silberstreifen, D-76287, Rheinstetten, Germany
| | - Claudio Luchinat
- Giotto Biotech S.R.L., Via Madonna del Piano 6, 50019, Sesto Fiorentino (FI), Italy.
- Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019, Sesto Fiorentino (FI), Italy.
- Department of Chemistry Ugo Schiff, University of Florence, Via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy.
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30
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Costa Pereira J, Jarak I, Carvalho RA. Resolving NMR signals of short-chain fatty acid mixtures using unsupervised component analysis. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2017; 55:936-943. [PMID: 28480544 DOI: 10.1002/mrc.4606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 04/28/2017] [Accepted: 05/03/2017] [Indexed: 06/07/2023]
Abstract
Nuclear magnetic resonance (NMR) is a very powerful instrumental technique suited to identify and characterize organic compounds. NMR has been successfully used in the analysis of complex biological and environmental samples; however, these applications are still rather limited. In this work, we describe unsupervised component analysis as a multivariate unsupervised method suited to identify the number of relevant NMR signal contributions and to deconvolve mixed signals into signal individual sources and respective contributions. Using this approach, we were able to advance further in the field of quantification of NMR spectra, and this methodology will help in the characterization of complex biological samples. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Jorge Costa Pereira
- CQC, Department of Chemistry, University of Coimbra, P-3004 535, Coimbra, Portugal
| | - Ivana Jarak
- CICS-UBI, University of Beira Interior, 6201-506, Covilha, Portugal
- Centre for Functional Ecology, Faculty of Sciences and Technology, University of Coimbra, P-3000 456, Coimbra, Portugal
| | - Rui Albuquerque Carvalho
- Centre for Functional Ecology, Faculty of Sciences and Technology, University of Coimbra, P-3000 456, Coimbra, Portugal
- Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, P-3000 456, Coimbra, Portugal
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31
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Louis E, Cantrelle FX, Mesotten L, Reekmans G, Bervoets L, Vanhove K, Thomeer M, Lippens G, Adriaensens P. Metabolic phenotyping of human plasma by 1 H-NMR at high and medium magnetic field strengths: a case study for lung cancer. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2017; 55:706-713. [PMID: 28061019 DOI: 10.1002/mrc.4577] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 12/25/2016] [Accepted: 01/04/2017] [Indexed: 06/06/2023]
Abstract
Accurate identification and quantification of human plasma metabolites can be challenging in crowded regions of the NMR spectrum with severe signal overlap. Therefore, this study describes metabolite spiking experiments on the basis of which the NMR spectrum can be rationally segmented into well-defined integration regions, and this for spectrometers having magnetic field strengths corresponding to 1 H resonance frequencies of 400 MHz and 900 MHz. Subsequently, the integration data of a case-control dataset of 69 lung cancer patients and 74 controls were used to train a multivariate statistical classification model for both field strengths. In this way, the advantages/disadvantages of high versus medium magnetic field strength were evaluated. The discriminative power obtained from the data collected at the two magnetic field strengths is rather similar, i.e. a sensitivity and specificity of respectively 90 and 97% for the 400 MHz data versus 88 and 96% for the 900 MHz data. This shows that a medium-field NMR spectrometer (400-600 MHz) is already sufficient to perform clinical metabolomics. However, the improved spectral resolution (reduced signal overlap) and signal-to-noise ratio of 900 MHz spectra yield more integration regions that represent a single metabolite. This will simplify the unraveling and understanding of the related, disease disturbed, biochemical pathways. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Evelyne Louis
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
| | - Francois-Xavier Cantrelle
- CNRS UMR 8576, Unité de Glycobiologie Structurale et Fonctionnelle, Université des Sciences et Technologies de Lille 1, Cité Scientifique, 59655, Villeneuve d'Ascq Cedex, France
| | - Liesbet Mesotten
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
- Department of Nuclear Medicine, Ziekenhuis Oost-Limburg, Schiepse Bos 6, 3600, Genk, Belgium
| | - Gunter Reekmans
- Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium
| | - Liene Bervoets
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
| | - Karolien Vanhove
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
- Department of Respiratory Medicine, Algemeen Ziekenhuis Vesalius, Hazelereik 51, 3700, Tongeren, Belgium
| | - Michiel Thomeer
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
- Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Schiepse Bos 6, 3600, Genk, Belgium
| | - Guy Lippens
- CNRS UMR 8576, Unité de Glycobiologie Structurale et Fonctionnelle, Université des Sciences et Technologies de Lille 1, Cité Scientifique, 59655, Villeneuve d'Ascq Cedex, France
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, INSA, University of Toulouse, CNRS, INRA, 135 Avenue de Rangueil, 31400, Toulouse, France
| | - Peter Adriaensens
- Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium
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Kubicek-Sutherland JZ, Vu DM, Mendez HM, Jakhar S, Mukundan H. Detection of Lipid and Amphiphilic Biomarkers for Disease Diagnostics. BIOSENSORS-BASEL 2017; 7:bios7030025. [PMID: 28677660 PMCID: PMC5618031 DOI: 10.3390/bios7030025] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 06/27/2017] [Accepted: 06/30/2017] [Indexed: 12/24/2022]
Abstract
Rapid diagnosis is crucial to effectively treating any disease. Biological markers, or biomarkers, have been widely used to diagnose a variety of infectious and non-infectious diseases. The detection of biomarkers in patient samples can also provide valuable information regarding progression and prognosis. Interestingly, many such biomarkers are composed of lipids, and are amphiphilic in biochemistry, which leads them to be often sequestered by host carriers. Such sequestration enhances the difficulty of developing sensitive and accurate sensors for these targets. Many of the physiologically relevant molecules involved in pathogenesis and disease are indeed amphiphilic. This chemical property is likely essential for their biological function, but also makes them challenging to detect and quantify in vitro. In order to understand pathogenesis and disease progression while developing effective diagnostics, it is important to account for the biochemistry of lipid and amphiphilic biomarkers when creating novel techniques for the quantitative measurement of these targets. Here, we review techniques and methods used to detect lipid and amphiphilic biomarkers associated with disease, as well as their feasibility for use as diagnostic targets, highlighting the significance of their biochemical properties in the design and execution of laboratory and diagnostic strategies. The biochemistry of biological molecules is clearly relevant to their physiological function, and calling out the need for consideration of this feature in their study, and use as vaccine, diagnostic and therapeutic targets is the overarching motivation for this review.
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Affiliation(s)
- Jessica Z Kubicek-Sutherland
- Physical Chemistry and Applied Spectroscopy, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Dung M Vu
- Physical Chemistry and Applied Spectroscopy, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Heather M Mendez
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
- The New Mexico Consortium, Los Alamos, NM 87544, USA.
| | - Shailja Jakhar
- Physical Chemistry and Applied Spectroscopy, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Harshini Mukundan
- Physical Chemistry and Applied Spectroscopy, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
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Kostidis S, Addie RD, Morreau H, Mayboroda OA, Giera M. Quantitative NMR analysis of intra- and extracellular metabolism of mammalian cells: A tutorial. Anal Chim Acta 2017. [PMID: 28622799 DOI: 10.1016/j.aca.2017.05.011] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Metabolomics analysis of body fluids as well as cells is depended on many factors. While several well-accepted standard operating procedures for the analysis of body fluids are available, the NMR based quantitative analysis of cellular metabolites is less well standardized. Experimental designs depend on the cell type, the quenching protocol and the applied post-acquisition workflow. Here, we provide a tutorial for the quantitative description of the metabolic phenotype of mammalian cells using NMR spectroscopy. We discuss all key steps of the process, starting from the selection of the appropriate culture medium, quenching techniques to arrest metabolism in a reproducible manner, the extraction of the intracellular components and the profiling of the culture medium. NMR data acquisition and methods for both qualitative and quantitative analysis are also provided. The suggested methods cover experiments for adherent cells and cells in suspension. We ultimately describe the application of the discussed workflow to a thyroid cancer cell line. Although this tutorial focuses on mammalian cells, the given guidelines and procedures may be adjusted for the analysis of other cell types.
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Affiliation(s)
- Sarantos Kostidis
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands.
| | - Ruben D Addie
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands; Department of Pathology, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
| | - Hans Morreau
- Department of Pathology, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
| | - Oleg A Mayboroda
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
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Metabolomics for empirical delineation of the traditional Korean fermented foods and beverages. Trends Food Sci Technol 2017. [DOI: 10.1016/j.tifs.2017.01.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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35
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Mach N, Ramayo-Caldas Y, Clark A, Moroldo M, Robert C, Barrey E, López JM, Le Moyec L. Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses. BMC Genomics 2017; 18:187. [PMID: 28212624 PMCID: PMC5316211 DOI: 10.1186/s12864-017-3571-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Accepted: 02/09/2017] [Indexed: 02/13/2023] Open
Abstract
Background Endurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise. For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition. Results We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition. Conclusions To the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3571-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Núria Mach
- Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
| | - Yuliaxis Ramayo-Caldas
- Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Allison Clark
- Health Science Department, Open University of Catalonia (UOC), Barcelona, Spain
| | - Marco Moroldo
- Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Céline Robert
- Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.,Paris-Est University, National Veterinary School of Alfort, Maisons-Alfort, France
| | - Eric Barrey
- Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Jesús Maria López
- Health Science Department, Open University of Catalonia (UOC), Barcelona, Spain
| | - Laurence Le Moyec
- Integrative Biology of Exercise Adaptations unit, UBIAE, EA7362, Evry Val d'Essone University, Evry, France
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Jacob D, Deborde C, Lefebvre M, Maucourt M, Moing A. NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. Metabolomics 2017; 13:36. [PMID: 28261014 PMCID: PMC5313591 DOI: 10.1007/s11306-017-1178-y] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 02/06/2017] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks. OBJECTIVES The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills. METHODS NMRProcFlow was developed to be a graphical and interactive 1D NMR (1H & 13C) spectra processing tool. RESULTS NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment. CONCLUSION Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.
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Affiliation(s)
- D. Jacob
- UMR1332 Fruit Biology and Pathology, INRA, Univ. Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 71 avenue Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - C. Deborde
- UMR1332 Fruit Biology and Pathology, INRA, Univ. Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 71 avenue Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - M. Lefebvre
- UMR1332 Fruit Biology and Pathology, INRA, Univ. Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 71 avenue Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - M. Maucourt
- UMR1332 Fruit Biology and Pathology, INRA, Univ. Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 71 avenue Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - A. Moing
- UMR1332 Fruit Biology and Pathology, INRA, Univ. Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 71 avenue Edouard Bourlaux, 33140 Villenave d’Ornon, France
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Li S, Klencsár B, Balcaen L, Cuyckens F, Lynen F, Vanhaecke F. Quantitative Metabolite Profiling of an Amino Group Containing Pharmaceutical in Human Plasma via Precolumn Derivatization and High-Performance Liquid Chromatography-Inductively Coupled Plasma Mass Spectrometry. Anal Chem 2017; 89:1907-1915. [PMID: 28050907 DOI: 10.1021/acs.analchem.6b04388] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Quantitative determination of the candidate drug molecule and its metabolites in biofluids and tissues is an inevitable step in the development of new pharmaceuticals. Because of the time-consuming and expensive nature of the current standard technique for quantitative metabolite profiling, i.e., radiolabeling followed by high-performance liquid chromatography (HPLC) with radiodetection, the development of alternative methodologies is of great interest. In this work, a simple, fast, sensitive, and accurate method for the quantitative metabolite profiling of an amino group containing drug (levothyroxine) and its metabolites in human plasma, based on precolumn derivatization followed by HPLC-inductively coupled plasma mass spectrometry (ICPMS), was developed and validated. To introduce a suitable "heteroelement" (defined here as an element that is detectable with ICPMS), an inexpensive and commercially available reagent, tetrabromophthalic anhydride (TBPA) was used for the derivatization of free NH2-groups. The presence of a known number of I atoms in both the drug molecule and its metabolites enabled a cross-validation of the newly developed derivatization procedure and quantification based on monitoring of the introduced Br. The formation of the derivatives was quantitative, providing a 4:1 stoichiometric Br/NH2 ratio. The derivatives were separated via reversed-phase HPLC with gradient elution. Bromine was determined via ICPMS at a mass-to-charge ratio of 79 using H2 as a reaction gas to ensure interference-free detection, and iodine was determined at a mass-to-charge ratio of 127 for cross-validation purposes. The method developed shows a fit-for-purpose accuracy (recovery between 85% and 115%) and precision (repeatability <15% RSD). The limit of quantification (LoQ) for Br was approximately 100 μg/L.
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Affiliation(s)
- Sanwang Li
- Department of Analytical Chemistry, Ghent University , Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Balázs Klencsár
- Department of Analytical Chemistry, Ghent University , Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Lieve Balcaen
- Department of Analytical Chemistry, Ghent University , Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
| | - Filip Cuyckens
- Pharmacokinetics, Dynamics & Metabolism, Janssen R&D , Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Frederic Lynen
- Department of Organic and Macromolecular Chemistry, Ghent University , Campus Sterre, Krijgslaan 281-S4-bis, 9000 Ghent, Belgium
| | - Frank Vanhaecke
- Department of Analytical Chemistry, Ghent University , Campus Sterre, Krijgslaan 281-S12, 9000 Ghent, Belgium
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Kikuchi J, Yamada S. NMR window of molecular complexity showing homeostasis in superorganisms. Analyst 2017; 142:4161-4172. [DOI: 10.1039/c7an01019b] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
| | - Shunji Yamada
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
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Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis. Metabolites 2016; 6:metabo6040047. [PMID: 28009836 PMCID: PMC5192453 DOI: 10.3390/metabo6040047] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 12/03/2016] [Accepted: 12/13/2016] [Indexed: 12/29/2022] Open
Abstract
Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate.
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Metabolomic analysis of urine with Nuclear Magnetic Resonance spectroscopy in patients with idiopathic sudden sensorineural hearing loss: A preliminary study. Auris Nasus Larynx 2016; 44:381-389. [PMID: 27817938 DOI: 10.1016/j.anl.2016.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 09/22/2016] [Accepted: 10/06/2016] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Idiopathic sudden sensorineural hearing loss is a frequent emergency, with unknown aetiology and usually treated with empiric therapy. Steroids represent the only validated treatment but prognosis is unpredictable and the possibility to select the patients who will not respond to steroids could avoid unnecessary treatments. Metabolomic profiling of the biofluids target the analysis of the final product of genic expression and enzymatic activity, defining the biochemical phenotype of a whole biologic system. METHODS We studied the metabolomics of the urine of a cohort of patients with idiopathic sudden sensorineural hearing loss, correlating the metabolic profiles with the clinical outcomes. Metabolomic profiling of urine samples was performed by 1H Nuclear Magnetic Resonance spectroscopy in combination with multivariate statistical approaches. RESULTS 26 patients were included in the study: 5 healthy controls, 13 patients who did not recover after treatment at 6 months while the remaining 8 patients recovered from the hearing loss. The orthogonal partial least square-discriminant analysis score plot showed a significant separation between the two groups, responders and non-responders after steroid therapy, R2Y of 0.83, Q2 of 0.38 and p value <0.05. The resulting metabolic profiles were characterized by higher levels of urinary B-Alanine, 3-hydroxybutyrate and Trimethylamine N-oxide, and lower levels of Citrate and Creatinine in patients with worst outcome. CONCLUSION Idiopathic sudden sensorineural hearing loss is a specific disease with unclear systemic changes, but our data suggest that there are different types of this disorder or patients predisposed to effective action of steroids allowing the recover after treatment.
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van Duynhoven JPM, Jacobs DM. Assessment of dietary exposure and effect in humans: The role of NMR. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2016; 96:58-72. [PMID: 27573181 DOI: 10.1016/j.pnmrs.2016.03.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2015] [Revised: 03/19/2016] [Accepted: 03/19/2016] [Indexed: 06/06/2023]
Abstract
In human nutritional science progress has always depended strongly on analytical measurements for establishing relationships between diet and health. This field has undergone significant changes as a result of the development of NMR and mass spectrometry methods for large scale detection, identification and quantification of metabolites in body fluids. This has allowed systematic studies of the metabolic fingerprints that biological processes leave behind, and has become the research field of metabolomics. As a metabolic profiling technique, NMR is at its best when its unbiased nature, linearity and reproducibility are exploited in well-controlled nutritional intervention and cross-sectional population screening studies. Although its sensitivity is less good than that of mass spectrometry, NMR has maintained a strong position in metabolomics through implementation of standardisation protocols, hyphenation with mass spectrometry and chromatographic techniques, accurate quantification and spectral deconvolution approaches, and high-throughput automation. Thus, NMR-based metabolomics has contributed uniquely to new insights into dietary exposure, in particular by unravelling the metabolic fates of phytochemicals and the discovery of dietary intake markers. NMR profiling has also contributed to the understanding of the subtle effects of diet on central metabolism and lipoprotein metabolism. In order to hold its ground in nutritional metabolomics, NMR will need to step up its performance in sensitivity and resolution; the most promising routes forward are the analytical use of dynamic nuclear polarisation and developments in microcoil construction and automated fractionation.
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Affiliation(s)
- John P M van Duynhoven
- Unilever R&D Vlaardingen, Olivier van Noortlaan 120, 3130AC Vlaardingen, The Netherlands; Laboratory of Biophysics and Wageningen NMR Centre, Wageningen University, Dreijenlaan 3, 6703HA Wageningen, The Netherlands.
| | - Doris M Jacobs
- Unilever R&D Vlaardingen, Olivier van Noortlaan 120, 3130AC Vlaardingen, The Netherlands
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Software-assisted serum metabolite quantification using NMR. Anal Chim Acta 2016; 934:194-202. [PMID: 27506360 DOI: 10.1016/j.aca.2016.04.054] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 04/27/2016] [Accepted: 04/29/2016] [Indexed: 01/12/2023]
Abstract
The goal of metabolomics is to analyze a whole metabolome under a given set of conditions, and accurate and reliable quantitation of metabolites is crucial. Absolute concentration is more valuable than relative concentration; however, the most commonly used method in NMR-based serum metabolic profiling, bin-based and full data point peak quantification, provides relative concentration levels of metabolites and are not reliable when metabolite peaks overlap in a spectrum. In this study, we present the software-assisted serum metabolite quantification (SASMeQ) method, which allows us to identify and quantify metabolites in NMR spectra using Chenomx software. This software uses the ERETIC2 utility from TopSpin to add a digitally synthesized peak to a spectrum. The SASMeQ method will advance NMR-based serum metabolic profiling by providing an accurate and reliable method for absolute quantification that is superior to bin-based quantification.
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Chen Z, Kim J. Urinary proteomics and metabolomics studies to monitor bladder health and urological diseases. BMC Urol 2016; 16:11. [PMID: 27000794 PMCID: PMC4802825 DOI: 10.1186/s12894-016-0129-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 03/10/2016] [Indexed: 12/16/2022] Open
Abstract
Background Assays of molecular biomarkers in urine are non-invasive compared to other body fluids and can be easily repeated. Based on the hypothesis that the secreted markers from the diseased organs may locally release into the body fluid in the vicinity of the injury, urine-based assays have been considered beneficial to monitoring bladder health and urological diseases. The urine proteome is much less complex than the serum and tissues, but nevertheless can contain biomarkers for diagnosis and prognosis of diseases. The urine metabolome has a much higher number and concentration of low-molecular metabolites than the serum or tissues, with a far lower lipid concentration, yet informs directly about dietary and microbial metabolism. Discussion We here discuss the use of mass spectrometry-based proteomics and metabolomics for urine biomarker assays, specifically with respect to the underlying mechanisms that trigger the pathological condition. Conclusion Molecular biomarker profiles, based on proteomics and metabolomics studies, reliably distinguish patients from healthy controls, stratify sub-populations with respect to treatment options, and predict therapeutic response of patients with urological disease.
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Affiliation(s)
- Zhaohui Chen
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jayoung Kim
- Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA. .,Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA. .,Department of Medicine, University of California, Los Angeles, CA, USA.
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Emwas AH, Roy R, McKay RT, Ryan D, Brennan L, Tenori L, Luchinat C, Gao X, Zeri AC, Gowda GAN, Raftery D, Steinbeck C, Salek RM, Wishart DS. Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis. J Proteome Res 2016; 15:360-73. [PMID: 26745651 PMCID: PMC4865177 DOI: 10.1021/acs.jproteome.5b00885] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.
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Affiliation(s)
- Abdul-Hamid Emwas
- Imaging and Characterization Core Lab, KAUST , Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Raja Roy
- Centre of Biomedical Research, formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus , Lucknow, Uttar Pradesh, India
| | - Ryan T McKay
- Department of Chemistry, University of Alberta , Edmonton, Alberta, Canada
| | - Danielle Ryan
- School of Agricultural and Wine Sciences, Charles Sturt University , Bathurst, New South Wales, Australia
| | - Lorraine Brennan
- UCD Insitute of Food and Health, UCD , Belfield, Dublin, Ireland
| | - Leonardo Tenori
- FiorGen Foundation , 50019 Sesto Fiorentino, Florence, Italy
| | - Claudio Luchinat
- Centro Risonanze Magnetiche - CERM, University of Florence , Florence, Italy
| | - Xin Gao
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ana Carolina Zeri
- Brazilian Biosciences National Laboratory, LNBio , Campinas, São Paulo, Brazil
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States.,Fred Hutchinson Cancer Research Center , 1100 Fairview Avenue, Seattle, Washington 98109, United States
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - David S Wishart
- Department of Biological Sciences, University of Alberta , Edmonton, Alberta, Canada
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Bag S, Dutta D, Chaudhary A, Chandra Sing B, Banerjee R, Pal M, Paul RR, Basak A, Das AK, Ray AK, Chatterjee J. NanoLC MALDI MS/MS based quantitative metabolomics reveals the alteration of membrane biogenesis in oral cancer. RSC Adv 2016. [DOI: 10.1039/c6ra07001a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
We present a label-free untargeted metabolomics approach using nanoLC-MALDI MS/MS interface for the separation, identification and quantification of the metabolites from cancer biopsies.
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Affiliation(s)
- Swarnendu Bag
- School of Medical Science and Technology
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Debabrata Dutta
- Department of Biotechnology
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Amrita Chaudhary
- School of Medical Science and Technology
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Bidhan Chandra Sing
- Central Research Facility
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Rita Banerjee
- Department of Science and Technology
- New Delhi 110016
- India
| | - Mousumi Pal
- Department of Oral and Maxillofacial Pathology
- Guru Nanak Institute of Dental Sciences and Research
- Kolkata 700114
- India
| | - Ranjan Rashmi Paul
- Department of Oral and Maxillofacial Pathology
- Guru Nanak Institute of Dental Sciences and Research
- Kolkata 700114
- India
| | - Amit Basak
- Department of Chemistry
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Amit Kumar Das
- Department of Biotechnology
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
| | - Ajoy Kumar Ray
- Indian Institute of Engineering Science and Technology
- Howrah 711103
- India
| | - Jyotirmoy Chatterjee
- School of Medical Science and Technology
- Indian Institute of Technology Kharagpur
- Kharagpur 721302
- India
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Puchades-Carrasco L, Palomino-Schätzlein M, Pérez-Rambla C, Pineda-Lucena A. Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers. Brief Bioinform 2015; 17:541-52. [PMID: 26342127 DOI: 10.1093/bib/bbv077] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Indexed: 12/29/2022] Open
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Simultaneous acquisition of three NMR spectra in a single experiment for rapid resonance assignments in metabolomics. J CHEM SCI 2015. [DOI: 10.1007/s12039-015-0868-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Worley B, Powers R. Generalized adaptive intelligent binning of multiway data. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2015; 146:42-46. [PMID: 26052171 PMCID: PMC4456038 DOI: 10.1016/j.chemolab.2015.05.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
NMR metabolic fingerprinting methods almost exclusively rely upon the use of one-dimensional (1D) 1H NMR data to gain insights into chemical differences between two or more experimental classes. While 1D 1H NMR spectroscopy is a powerful, highly informative technique that can rapidly and nondestructively report details of complex metabolite mixtures, it suffers from significant signal overlap that hinders interpretation and quantification of individual analytes. Two-dimensional (2D) NMR methods that report heteronuclear connectivities can reduce spectral overlap, but their use in metabolic fingerprinting studies is limited. We describe a generalization of Adaptive Intelligent binning that enables its use on multidimensional datasets, allowing the direct use of nD NMR spectroscopic data in bilinear factorizations such as principal component analysis (PCA) and partial least squares (PLS).
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Affiliation(s)
| | - Robert Powers
- To whom correspondence should be addressed Robert Powers University of Nebraska-Lincoln Department of Chemistry 722 Hamilton Hall Lincoln, NE 68588-0304 Phone: (402) 472-3039 Fax: (402) 472-9402
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Alonso A, Marsal S, Julià A. Analytical methods in untargeted metabolomics: state of the art in 2015. Front Bioeng Biotechnol 2015; 3:23. [PMID: 25798438 PMCID: PMC4350445 DOI: 10.3389/fbioe.2015.00023] [Citation(s) in RCA: 393] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/18/2015] [Indexed: 12/20/2022] Open
Abstract
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
- Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
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50
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Hilty C, Ragavan M. Application of blind source separation to real-time dissolution dynamic nuclear polarization. Anal Chem 2015; 87:1004-8. [PMID: 25506716 DOI: 10.1021/ac503475c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The use of a blind source separation (BSS) algorithm is demonstrated for the analysis of time series of nuclear magnetic resonance (NMR) spectra. This type of data is obtained commonly from experiments, where analytes are hyperpolarized using dissolution dynamic nuclear polarization (D-DNP), both in in vivo and in vitro contexts. High signal gains in D-DNP enable rapid measurement of data sets characterizing the time evolution of chemical or metabolic processes. BSS is based on an algorithm that can be applied to separate the different components contributing to the NMR signal and determine the time dependence of the signals from these components. This algorithm requires minimal prior knowledge of the data, notably, no reference spectra need to be provided, and can therefore be applied rapidly. In a time-resolved measurement of the enzymatic conversion of hyperpolarized oxaloacetate to malate, the two signal components are separated into computed source spectra that closely resemble the spectra of the individual compounds. An improvement in the signal-to-noise ratio of the computed source spectra is found compared to the original spectra, presumably resulting from the presence of each signal more than once in the time series. The reconstruction of the original spectra yields the time evolution of the contributions from the two sources, which also corresponds closely to the time evolution of integrated signal intensities from the original spectra. BSS may therefore be an approach for the efficient identification of components and estimation of kinetics in D-DNP experiments, which can be applied at a high level of automation.
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Affiliation(s)
- Christian Hilty
- Department of Chemistry and ‡Department of Biochemistry and Biophysics, Texas A&M University , College Station, Texas 77843, United States
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