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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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2
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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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3
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Mallol R, Vallvé JC, Solà R, Girona J, Bergmann S, Correig X, Rock E, Winklhofer-Roob BM, Rehues P, Guardiola M, Masana L, Ribalta J. Statistical mediation of the relationships between chronological age and lipoproteins by nonessential amino acids in healthy men. Comput Struct Biotechnol J 2021; 19:6169-6178. [PMID: 34900130 PMCID: PMC8632714 DOI: 10.1016/j.csbj.2021.11.022] [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: 07/16/2021] [Revised: 10/26/2021] [Accepted: 11/14/2021] [Indexed: 12/21/2022] Open
Abstract
Aging is a major risk factor for metabolic impairment that may lead to age-related diseases such as cardiovascular disease. Different mechanisms that may explain the interplay between aging and lipoproteins, and between aging and low-molecular-weight metabolites (LMWMs), in the metabolic dysregulation associated with age-related diseases have been described separately. Here, we statistically evaluated the possible mediation effects of LMWMs on the relationships between chronological age and lipoprotein concentrations in healthy men ranging from 19 to 75 years of age. Relative and absolute concentrations of LMWMs and lipoproteins, respectively, were assessed by nuclear magnetic resonance (NMR) spectroscopy. Multivariate linear regression and mediation analysis were conducted to explore the associations between age, lipoproteins and LMWMs. The statistical significance of the identified mediation effects was evaluated using the bootstrapping technique, and the identified mediation effects were validated on a publicly available dataset. Chronological age was statistically associated with five lipoprotein classes and subclasses. The mediation analysis showed that serine mediated 24.1% (95% CI: 22.9 – 24.7) of the effect of age on LDL-P, and glutamate mediated 17.9% (95% CI: 17.6 – 18.5) of the effect of age on large LDL-P. In the publicly available data, glutamate mediated the relationship between age and an NMR-derived surrogate of cholesterol. Our results suggest that the age-related increase in LDL particles may be mediated by a decrease in the nonessential amino acid glutamate. Future studies may contribute to a better understanding of the potential biological role of glutamate and LDL particles in aging mechanisms and age-related diseases.
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Affiliation(s)
- Roger Mallol
- La Salle, Ramon Llull University, Barcelona, Spain.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joan Carles Vallvé
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Rosa Solà
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Josefa Girona
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Xavier Correig
- Metabolomics Platform, Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Edmond Rock
- UMMM, INRA-Theix, St. Genes Champanelle, France
| | - Brigitte M Winklhofer-Roob
- Human Nutrition and Metabolism Research and Training Center, Institute of Molecular Biosciences, Karl-Franzens University, Graz, Austria
| | - Pere Rehues
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Montse Guardiola
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Lluís Masana
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
| | - Josep Ribalta
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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4
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MacDonald R, Sokolenko S. Detection of highly overlapping peaks via adaptive apodization. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 333:107104. [PMID: 34801821 DOI: 10.1016/j.jmr.2021.107104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/20/2021] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
Accurate peak detection is an essential component of many NMR tasks such as peak alignment, compound identification, and global spectral deconvolution. However, current peak detection approaches are generally limited by their ability to deal with spectral overlap, which has a deleterious effect on downstream data processing. In this work, we present the use of an adaptive apodization strategy that improves the detection of highly overlapping peaks. Sensitivity enhancement is used to identify general regions of interest and resolution enhancement is used to separate overlapping peaks, with parameters for both calculated directly from the data. Further limits on peak width help to reduce false positives. The method proposed in this work has been implemented in an open-source R package called rnmrfind that is available for download on GitHub (https://github.com/ssokolen/rnmrfind). A set of default parameters have been chosen to provide effective peak detection while keeping false positives to a minimum; however, application-specific tuning is possible through the modification of minimum peak width at half height (in Hz) and noise cutoff threshold (as a factor of estimated standard deviation). Comparison to existing packages rNMR and speaq on a series of 1H NMR spectra demonstrates improved peak resolution with little to no apparent drawbacks.
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Affiliation(s)
- Ruis MacDonald
- Process Engineering and Applied Science, Dalhousie University, Sexton Campus, 5273 DaCosta Row, PO Box 15000, Halifax NS B3H 4R2, Canada
| | - Stanislav Sokolenko
- Process Engineering and Applied Science, Dalhousie University, Sexton Campus, 5273 DaCosta Row, PO Box 15000, Halifax NS B3H 4R2, Canada.
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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6
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Abstract
In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.
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Affiliation(s)
- Wimal Pathmasiri
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA.
| | - Kristine Kay
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan McRitchie
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan Sumner
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
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7
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Khalili B, Tomasoni M, Mattei M, Mallol Parera R, Sonmez R, Krefl D, Rueedi R, Bergmann S. Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites. J Proteome Res 2019; 18:3360-3368. [PMID: 31318216 DOI: 10.1021/acs.jproteome.9b00295] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.
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Affiliation(s)
- Bita Khalili
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Mattia Tomasoni
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Mirjam Mattei
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Roger Mallol Parera
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Reyhan Sonmez
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Daniel Krefl
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Rico Rueedi
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland
| | - Sven Bergmann
- Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.,Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.,Department of Integrative Biomedical Sciences , University of Cape Town , Cape Town 7700 , South Africa
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8
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Schripsema J. Similarity and differential NMR spectroscopy in metabolomics: application to the analysis of vegetable oils with 1H and 13C NMR. Metabolomics 2019; 15:39. [PMID: 30843128 DOI: 10.1007/s11306-019-1502-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 02/23/2019] [Indexed: 02/06/2023]
Abstract
INTRODUCTION In NMR based metabolomics there is a need for tools to easily compare spectra and to extract the maximum of information from the data. OBJECTIVES The calculation of similarity and performing differential NMR spectroscopy provides important additional information for classification and validation in metabolomics experiments. METHODS From 13 different vegetable oils samples were analysed by 1H and 13C NMR. The similarity between spectra was calculated and differential NMR spectroscopy was used to discover marker compounds. RESULTS The similarity between the individual spectra was calculated for the spectra of all samples. The similarity was used to verify and improve the alignment. For vegetable oils which showed a high similarity, e.g. chia seed oil and linseed oil, differential NMR spectroscopy was used to discover marker compounds. CONCLUSIONS The calculation of similarity is an important tool to reveal variability between samples and spectra and can be used to verify data sets and improve alignment or binning procedures. With differential spectroscopy marker compounds are easily discovered. The methods can be seen as an important addition to the routine procedures of metabolomics experiments.
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Affiliation(s)
- Jan Schripsema
- Grupo Metabolômica, Laboratório de Ciências Quimicas, Universidade Estadual do Norte Fluminense, Av. Alberto Lamego, 2000, Campos dos Goytacazes, RJ, 28013-602, Brazil.
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9
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Friedrich N, Skaaby T, Pietzner M, Budde K, Thuesen B, Nauck M, Linneberg A. Identification of urine metabolites associated with 5-year changes in biomarkers of glucose homoeostasis. DIABETES & METABOLISM 2018; 44:261-268. [DOI: 10.1016/j.diabet.2017.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 05/09/2017] [Accepted: 05/23/2017] [Indexed: 01/11/2023]
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10
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Rueedi R, Mallol R, Raffler J, Lamparter D, Friedrich N, Vollenweider P, Waeber G, Kastenmüller G, Kutalik Z, Bergmann S. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput Biol 2017; 13:e1005839. [PMID: 29194434 PMCID: PMC5711027 DOI: 10.1371/journal.pcbi.1005839] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 10/23/2017] [Indexed: 01/06/2023] Open
Abstract
A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
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Affiliation(s)
- Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Roger Mallol
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - David Lamparter
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site, Greifswald, Germany
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gérard Waeber
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- * E-mail:
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11
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Bowler RP, Wendt CH, Fessler MB, Foster MW, Kelly RS, Lasky-Su J, Rogers AJ, Stringer KA, Winston BW. New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2017; 14:1721-1743. [PMID: 29192815 PMCID: PMC5946579 DOI: 10.1513/annalsats.201710-770ws] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.
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12
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Monakhova YB, Mushtakova SP. Methodology of chemometric modeling of spectrometric signals in the analysis of complex samples. JOURNAL OF ANALYTICAL CHEMISTRY 2017. [DOI: 10.1134/s1061934816120066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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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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14
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Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C. Navigating freely-available software tools for metabolomics analysis. Metabolomics 2017; 13:106. [PMID: 28890673 PMCID: PMC5550549 DOI: 10.1007/s11306-017-1242-7] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 07/25/2017] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. OBJECTIVES To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. METHODS The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for. RESULTS A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary. CONCLUSION This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.
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Affiliation(s)
- Rachel Spicer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Reza M. Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Daniel Cañueto
- Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007 Tarragona, Catalonia Spain
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
- Friedrich-Schiller-University Jena, Lessingstr. 8, Jena, 07743 Germany
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15
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Alonso A, Julià A, Vinaixa M, Domènech E, Fernández-Nebro A, Cañete JD, Ferrándiz C, Tornero J, Gisbert JP, Nos P, Casbas AG, Puig L, González-Álvaro I, Pinto-Tasende JA, Blanco R, Rodríguez MA, Beltran A, Correig X, Marsal S. Urine metabolome profiling of immune-mediated inflammatory diseases. BMC Med 2016; 14:133. [PMID: 27609333 PMCID: PMC5016926 DOI: 10.1186/s12916-016-0681-8] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 08/25/2016] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis. METHODS Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. RESULTS In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. CONCLUSIONS This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain.
| | - Maria Vinaixa
- Centre for Omic Sciences, COS-DEEEA-URV-IISPV, Reus, Spain.,Metabolomics Platform, CIBERDEM, Reus, Spain
| | - Eugeni Domènech
- Hospital Universitari Germans Trias i Pujol, Badalona, Spain.,CIBERehd, Madrid, Spain
| | - Antonio Fernández-Nebro
- UGC Reumatología, Instituto de Investigación Biomédica (IBIMA), Hospital Regional Universitario de Málaga, Universidad de Málaga, Málaga, Spain
| | - Juan D Cañete
- Hospital Clínic de Barcelona and IDIBAPS, Barcelona, Spain
| | | | - Jesús Tornero
- Hospital Universitario Guadalajara, Guadalajara, Spain
| | - Javier P Gisbert
- CIBERehd, Madrid, Spain.,Hospital Universitario de la Princesa and IIS-IP, Madrid, Spain
| | - Pilar Nos
- CIBERehd, Madrid, Spain.,Hospital la Fe, Valencia, Spain
| | | | - Lluís Puig
- Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | | | - Ricardo Blanco
- Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Miguel A Rodríguez
- Centre for Omic Sciences, COS-DEEEA-URV-IISPV, Reus, Spain.,Metabolomics Platform, CIBERDEM, Reus, Spain
| | - Antoni Beltran
- Centre for Omic Sciences, COS-DEEEA-URV-IISPV, Reus, Spain.,Metabolomics Platform, CIBERDEM, Reus, Spain
| | - Xavier Correig
- Centre for Omic Sciences, COS-DEEEA-URV-IISPV, Reus, Spain.,Metabolomics Platform, CIBERDEM, Reus, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron Hospital Research Institute, Barcelona, Spain.
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16
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Monakhova YB, Tsikin AM, Mushtakova SP. Processing of NMR, UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis. JOURNAL OF ANALYTICAL CHEMISTRY 2016. [DOI: 10.1134/s1061934816060113] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Misra BB, van der Hooft JJJ. Updates in metabolomics tools and resources: 2014-2015. Electrophoresis 2015; 37:86-110. [DOI: 10.1002/elps.201500417] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Revised: 10/04/2015] [Accepted: 10/05/2015] [Indexed: 12/12/2022]
Affiliation(s)
- Biswapriya B. Misra
- Department of Biology, Genetics Institute; University of Florida; Gainesville FL USA
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18
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Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality. PLoS Genet 2015; 11:e1005487. [PMID: 26352407 PMCID: PMC4564198 DOI: 10.1371/journal.pgen.1005487] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/06/2015] [Indexed: 12/24/2022] Open
Abstract
Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases. Human metabolism is influenced by genetic and environmental factors defining a person’s metabolic individuality. This individuality is linked to personal differences in the ability to react on metabolic challenges and in the susceptibility to specific diseases. By investigating how common variants in genetic regions (loci) affect individual blood metabolite levels, the substantial contribution of genetic inheritance to metabolic individuality has been demonstrated previously. Meanwhile, more than 150 loci influencing metabolic homeostasis in blood are known. Here we shift the focus to genetic variants that modulate urinary metabolite excretion, for which only 11 loci were reported so far. In the largest genetic study on urinary metabolites to date, we identified 15 additional loci. Most of the 26 loci also affect blood metabolite levels. This shows that the metabolic individuality seen in blood is also reflected in urine, which is expected when urine is regarded as “diluted blood”. Nonetheless, we also found loci that appear to primarily influence metabolite excretion. For instance, we identified genetic variants near a gene of a transporter that change the capability for renal re-absorption of the transporter’s substrate. Thus, our findings could help to elucidate molecular mechanisms influencing kidney function and the body’s detoxification capabilities.
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19
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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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20
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Lamichhane S, Yde CC, Schmedes MS, Jensen HM, Meier S, Bertram HC. Strategy for Nuclear-Magnetic-Resonance-Based Metabolomics of Human Feces. Anal Chem 2015; 87:5930-7. [DOI: 10.1021/acs.analchem.5b00977] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Santosh Lamichhane
- Department
of Food Science, Aarhus University, Kirstinebjergvej 10, DK-5792 Aarslev, Denmark
| | - Christian C. Yde
- Department
of Food Science, Aarhus University, Kirstinebjergvej 10, DK-5792 Aarslev, Denmark
| | - Mette S. Schmedes
- Department
of Food Science, Aarhus University, Kirstinebjergvej 10, DK-5792 Aarslev, Denmark
| | - Henrik Max Jensen
- DuPont Nutrition
Biosciences ApS, Edwin Rahrsvej 38, 8220 Brabrand, Aarhus, Denmark
| | - Sebastian Meier
- Department
of Chemistry, Technical University of Denmark, Kemitorvet, Building 201, DK-2800 Kongens Lyngby, Denmark
| | - Hanne Christine Bertram
- Department
of Food Science, Aarhus University, Kirstinebjergvej 10, DK-5792 Aarslev, Denmark
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21
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Hedjazi L, Gauguier D, Zalloua PA, Nicholson JK, Dumas ME, Cazier JB. mQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles. Anal Chem 2015; 87:4377-84. [PMID: 25803548 DOI: 10.1021/acs.analchem.5b00145] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.
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Affiliation(s)
| | | | - Pierre A Zalloua
- ⊥School of Medicine, Lebanese American University, Beirut 1102 2801, Lebanon
| | - Jeremy K Nicholson
- ‡Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming building, London SW7 2AZ, U.K
| | - Marc-Emmanuel Dumas
- ‡Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming building, London SW7 2AZ, U.K
| | - Jean-Baptiste Cazier
- ∥Department of Oncology, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, U.K.,○Centre for Computational Biology, University of Birmingham, Haworth Building, Edgbaston B15 2TT, U.K
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22
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Nguyen PM, Lyathaud C, Vitrac O. A Two-Scale Pursuit Method for the Tailored Identification and Quantification of Unknown Polymer Additives and Contaminants by 1H NMR. Ind Eng Chem Res 2015. [DOI: 10.1021/ie503592z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Phuong-Mai Nguyen
- Chemistry
and Physical Chemistry of Materials Division, Laboratoire National de métrologie et d’Essais (LNE), 78197 Trappes Cedex, France
- INRA, UMR 1145 Ingénierie Procédés Aliments, Group “Interactions between Materials and Media in Contact”, F-91300, Massy, France
- AgroParisTech, UMR 1145 Ingénierie Procédés Aliments, Group “Interactions between Materials and Media in Contact”, F-91300, Massy, France
| | - Cédric Lyathaud
- Chemistry
and Physical Chemistry of Materials Division, Laboratoire National de métrologie et d’Essais (LNE), 78197 Trappes Cedex, France
| | - Olivier Vitrac
- INRA, UMR 1145 Ingénierie Procédés Aliments, Group “Interactions between Materials and Media in Contact”, F-91300, Massy, France
- AgroParisTech, UMR 1145 Ingénierie Procédés Aliments, Group “Interactions between Materials and Media in Contact”, F-91300, Massy, France
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23
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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: 388] [Impact Index Per Article: 43.1] [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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Abstract
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Data
handling in the field of NMR metabolomics has historically
been reliant on either in-house mathematical routines or long chains
of expensive commercial software. Thus, while the relatively simple
biochemical protocols of metabolomics maintain a low barrier to entry,
new practitioners of metabolomics experiments are forced to either
purchase expensive software packages or craft their own data handling
solutions from scratch. This inevitably complicates the standardization
and communication of data handling protocols in the field. We report
a newly developed open-source platform for complete NMR metabolomics
data handling, MVAPACK, and describe its application on an example
metabolic fingerprinting data set.
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Affiliation(s)
- Bradley Worley
- Department
of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska 68588-0304, United States
| | - Robert Powers
- Department
of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska 68588-0304, United States
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25
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Tiainen M, Soininen P, Laatikainen R. Quantitative Quantum Mechanical Spectral Analysis (qQMSA) of (1)H NMR spectra of complex mixtures and biofluids. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2014; 242:67-78. [PMID: 24607824 DOI: 10.1016/j.jmr.2014.02.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 12/24/2013] [Accepted: 02/06/2014] [Indexed: 05/24/2023]
Abstract
The quantitative interpretation of (1)H NMR spectra of mixtures like the biofluids is a demanding task due to spectral complexity and overlap. Complications may arise also from water suppression, T2-editing, protein interactions, relaxation differences of the species, experimental artifacts and, furthermore, the spectra may contain unknown components and macromolecular background which cannot be easily separated from baseline. In this work, tools and strategies for quantitative Quantum Mechanical Spectral Analysis (qQMSA) of (1)H NMR spectra from complex mixtures were developed and systematically assessed. In the present approach, the signals of well-defined, stoichiometric components are described by a QM model, while the background is described by a multiterm baseline function and the unknown signals using optimizable and adjustable lines, regular multiplets or any spectral structures which can be composed from spectral lines. Any prior knowledge available from the spectrum can also be added to the model. Fitting strategies for weak and strongly overlapping spectral systems were developed and assessed using two basic model systems, the metabolite mixtures without and with macromolecular (serum) background. The analyses show that if the spectra are measured in high-throughput manner, the consistent absolute quantification demands some calibration to compensate the different response factors of the protons and compounds. On the other hand, the results show that also the T2-edited spectra can be measured so that they obey well the QM rules. In general, qQMSA exploits and interprets the spectral information in maximal way taking full advantage from the QM properties of the spectra and, at the same time, offers chemical confidence which means that individual components can be identified with high confidence on the basis of their accurate spectral parameters.
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Affiliation(s)
- Mika Tiainen
- School of Pharmacy, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Pasi Soininen
- School of Pharmacy, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Reino Laatikainen
- School of Pharmacy, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland.
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