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Yang Q, Chen S, Jiang W, Mi L, Liu J, Hu Y, Ji X, Wang J, Zhu F. MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics. Anal Chem 2024; 96:1410-1418. [PMID: 38221713 DOI: 10.1021/acs.analchem.3c03212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.
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Affiliation(s)
- Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Shuman Chen
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Wenyu Jiang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Lan Mi
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jiarui Liu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yu Hu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xinglai Ji
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jun Wang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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2
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Fu J, Zhu F, Xu CJ, Li Y. Metabolomics meets systems immunology. EMBO Rep 2023; 24:e55747. [PMID: 36916532 PMCID: PMC10074123 DOI: 10.15252/embr.202255747] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/24/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.
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Affiliation(s)
- Jianbo Fu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
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3
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Du X, Dastmalchi F, Ye H, Garrett TJ, Diller MA, Liu M, Hogan WR, Brochhausen M, Lemas DJ. Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software. Metabolomics 2023; 19:11. [PMID: 36745241 DOI: 10.1007/s11306-023-01974-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/20/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Farhad Dastmalchi
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Hao Ye
- Health Science Center Libraries, University of Florida, Florida, USA
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Florida, USA
| | - Matthew A Diller
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
- Department of Obstetrics and Gynecology, University of Florida College of Medicine, Florida, Gainesville, United States.
- Center for Perinatal Outcomes Research, University of Florida College of Medicine, Gainesville, United States.
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4
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Benkeblia N. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry metabolomics platforms: Tools for plant oligosaccharides analysis. CARBOHYDRATE POLYMER TECHNOLOGIES AND APPLICATIONS 2023. [DOI: 10.1016/j.carpta.2023.100304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
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5
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Du X, Aristizabal-Henao JJ, Garrett TJ, Brochhausen M, Hogan WR, Lemas DJ. A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research. Metabolites 2022; 12:87. [PMID: 35050209 PMCID: PMC8779534 DOI: 10.3390/metabo12010087] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/25/2021] [Accepted: 01/10/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
| | | | - Timothy J. Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL 32610, USA;
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - William R. Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
| | - Dominick J. Lemas
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
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6
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Optimization of metabolomic data processing using NOREVA. Nat Protoc 2022; 17:129-151. [PMID: 34952956 DOI: 10.1038/s41596-021-00636-9] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022]
Abstract
A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.
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7
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Grapevine and Wine Metabolomics-Based Guidelines for FAIR Data and Metadata Management. Metabolites 2021; 11:metabo11110757. [PMID: 34822415 PMCID: PMC8618349 DOI: 10.3390/metabo11110757] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/29/2021] [Accepted: 10/30/2021] [Indexed: 01/12/2023] Open
Abstract
In the era of big and omics data, good organization, management, and description of experimental data are crucial for achieving high-quality datasets. This, in turn, is essential for the export of robust results, to publish reliable papers, make data more easily available, and unlock the huge potential of data reuse. Lately, more and more journals now require authors to share data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This work aims to provide a step-by-step guideline for the FAIR data and metadata management specific to grapevine and wine science. In detail, the guidelines include recommendations for the organization of data and metadata regarding (i) meaningful information on experimental design and phenotyping, (ii) sample collection, (iii) sample preparation, (iv) chemotype analysis, (v) data analysis (vi) metabolite annotation, and (vii) basic ontologies. We hope that these guidelines will be helpful for the grapevine and wine metabolomics community and that it will benefit from the true potential of data usage in creating new knowledge being revealed.
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8
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Pandohee J, Kyereh E, Kulshrestha S, Xu B, Mahomoodally MF. Review of the recent developments in metabolomics-based phytochemical research. Crit Rev Food Sci Nutr 2021:1-16. [PMID: 34672234 DOI: 10.1080/10408398.2021.1993127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Phytochemicals are important bioactive components present in natural products. Although the health benefits of many food products are well-known and accepted as a common knowledge, the identity of the main bioactive molecules and the mechanism by which they interact in the body of human are often unknown. It was only in the last 30 years when the field of metabolomics had matured that the identification of such molecules with bioactivity has been made possible through the development of instruments to separate and computational techniques to characterize complex samples. This in turn has enabled in vitro studies to quantify the biological activity of the respective phytochemical either in mice models or in humans. In this review, the importance of key dietary phytochemicals such as phenolic acids, flavonoids, carotenoids, resveratrol, curcumin, and capsaicinoids are discussed together with their potential functions for human health. Untargeted metabolomics, in particular, liquid chromatography mass spectrometry, is the most used method to isolate, identify and profile bioactive compounds in the study of phytochemicals in foods. The application of metabolomics in drug discovery is a common practice nowadays and has boosted the drug and/or supplement manufacturing sector.HighlightsPhytochemicals are beneficial compounds for human healthPhytochemicals are plant-based bioactive and obtainable from natural productsUntargeted metabolomics has boosted the discovery of phytochemicals from foodTargeted metabolomics is key in the authentication and screening of phytochemicalsMetabolomics of phytochemicals is reshaping the road to drug and supplement manufacture.
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Affiliation(s)
- Jessica Pandohee
- Centre for Crop and Disease Management, Curtin University, Perth, Western Australia, Australia.,Department of Health Sciences, Faculty of Science, University of Mauritius, Réduit, Mauritius
| | | | - Saurabh Kulshrestha
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, India
| | - Baojun Xu
- Food Science and Technology Program, BNU-HKBU United International College, Zhuhai, Guangdong, China
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9
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Arapitsas P, Ugliano M, Marangon M, Piombino P, Rolle L, Gerbi V, Versari A, Mattivi F. Use of Untargeted Liquid Chromatography-Mass Spectrometry Metabolome To Discriminate Italian Monovarietal Red Wines, Produced in Their Different Terroirs. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:13353-13366. [PMID: 32271564 PMCID: PMC7997580 DOI: 10.1021/acs.jafc.0c00879] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 04/09/2020] [Accepted: 04/09/2020] [Indexed: 06/11/2023]
Abstract
The aim of this project was to register, in a liquid chromatography-mass spectrometry-based untargeted single-batch analysis, the metabolome of 11 single-cultivar, single-vintage Italian red wines (Aglianico, Cannonau, Corvina, Montepulciano, Nebbiolo, Nerello, Primitivo, Raboso, Sagrantino, Sangiovese, and Teroldego) from 12 regions across Italy, each one produced in their terroirs under ad hoc legal frameworks to guarantee their quality and origin. The data provided indications regarding the similarity between the cultivars and highlighted a rich list of putative biomarkers of origin wines (pBOWs) characterizing each individual cultivar-terroir combination, where Primitivo, Teroldego, and Nebbiolo had the maximum number of unique pBOWs. The pBOWs included anthocyanins (Teroldego), flavanols (Aglianico, Sangiovese, Nerello, and Nebbiolo), amino acids and N-containing metabolites (Primitivo), hydroxycinnamates (Cannonau), and flavonols (Sangiovese). The raw data generated in this study are publicly available and, therefore, accessible and reusable as a baseline data set for future investigations.
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Affiliation(s)
- Panagiotis Arapitsas
- Department
of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Edmund Mach 1, 38010 San Michele all’Adige, Trentino, Italy
| | - Maurizio Ugliano
- Department
of Biotechnology, University of Verona, Cà Vignal 1, Strada le Grazie
15, 37134 Verona, Italy
| | - Matteo Marangon
- Department
of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell’Università 16, 35020 Legnaro, Padua, Italy
| | - Paola Piombino
- Department
of Agricultural Sciences, Division of Vine and Wine Sciences, University of Naples Federico II, Viale Italia, 83100 Avellino, Italy
| | - Luca Rolle
- Department
of Agricultural, Forest and Food Sciences, University of Turin, Largo Paolo Braccini 2, 10095 Grugliasco, Turin, Italy
| | - Vincenzo Gerbi
- Department
of Agricultural, Forest and Food Sciences, University of Turin, Largo Paolo Braccini 2, 10095 Grugliasco, Turin, Italy
| | - Andrea Versari
- Department
of Agricultural and Food Sciences, University
of Bologna, Piazza Goidanich
60, 47521 Cesena, Forlì-Cesena, Italy
| | - Fulvio Mattivi
- Department
of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Edmund Mach 1, 38010 San Michele all’Adige, Trentino, Italy
- Department
of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123 Povo, Trentino, Italy
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10
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Yang Q, Wang Y, Zhang Y, Li F, Xia W, Zhou Y, Qiu Y, Li H, Zhu F. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res 2020; 48:W436-W448. [PMID: 32324219 PMCID: PMC7319444 DOI: 10.1093/nar/gkaa258] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 03/21/2020] [Accepted: 04/04/2020] [Indexed: 12/23/2022] Open
Abstract
Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.
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Affiliation(s)
- Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Weiqi Xia
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation & The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation & The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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11
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Price EJ, Drapal M, Perez‐Fons L, Amah D, Bhattacharjee R, Heider B, Rouard M, Swennen R, Becerra Lopez‐Lavalle LA, Fraser PD. Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 101:1258-1268. [PMID: 31845400 PMCID: PMC7383867 DOI: 10.1111/tpj.14649] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/09/2019] [Accepted: 11/28/2019] [Indexed: 05/06/2023]
Abstract
Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large-scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics-based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on-going genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.
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Affiliation(s)
- Elliott J. Price
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
- Present address:
Masaryk UniversityBrno‐Bohunice625 00Czech Republic
| | - Margit Drapal
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
| | - Laura Perez‐Fons
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
| | - Delphine Amah
- International Institute of Tropical AgriculturePMB 5320IbadanNigeria
| | | | | | - Mathieu Rouard
- Bioversity InternationalParc Scientifique Agropolis II34397MontpellierFrance
| | - Rony Swennen
- Laboratory of Tropical Crop ImprovementDivision of Crop BiotechnicsKU LeuvenB‐3001LeuvenBelgium
- Bioversity InternationalWillem De Croylaan 42B‐3001LeuvenBelgium
- International Institute of Tropical Agriculture. C/0 The Nelson Mandela African Institution of Science and TechnologyP.O. Box 44ArushaTanzania
| | | | - Paul D. Fraser
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
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12
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Moro L, Da Ros A, da Mota RV, Purgatto E, Mattivi F, Arapitsas P. LC-MS untargeted approach showed that methyl jasmonate application on Vitis labrusca L. grapes increases phenolics at subtropical Brazilian regions. Metabolomics 2020; 16:18. [PMID: 31974665 DOI: 10.1007/s11306-020-1641-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 01/18/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Vitis labrusca L. grapes are largely cultivated in Brazil, but the tropical climate negatively affects the phenols content, especially anthocyanin. According to the projections of the incoming climatic changes, the climate of several viticulture zone might change to tropical. Therefore, researches are focusing on increasing grape phenols content; with methyl jasmonate application (MeJa) is considered a good alternative. OBJECTIVES The aim was to investigate with an untargeted approach the metabolic changes caused by the MeJa pre-harvest application on two Vitis labrusca L. cultivars grapes, both of them grown in two Brazilian regions. METHODS Isabel Precoce and Concord grapes cultivated under subtropical climate, in the south and southeast of Brazil, received MeJa pre-harvest treatment. Grape metabolome was extracted and analyzed with a MS based metabolomics protocol by UPLC-HRMS-QTOF. RESULTS Unsupervised data analysis revealed a clear separation between the two regions and the two cultivars, while supervised data analysis revealed biomarkers between the MeJa treatment group and the control group. Among the metabolites positively affected by MeJa were (a) flavonoids with a high degree of methylation at the B-ring (malvidin and peonidin derivatives and isorhamentin) for Isabel Precoce grapes; (b) glucosides of hydroxycinnamates, gallocatechin, epigallocatechin and cis-piceid for Concord grapes; and (c) hydroxycinnamates esters with tartaric acid, and procyanidins for the Southeast region grapes. CONCLUSION These results suggest that MeJa can be used as elicitor to secondary metabolism in grapes grown even under subtropical climate, affecting phenolic biosynthesis.
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Affiliation(s)
- Laís Moro
- Deptartment of Food Science and Experimental Nutrition/FORC - Food Research Center, University of São Paulo, São Paulo, Brazil.
| | - Alessio Da Ros
- Department of Food Quality and Nutrition, Research and Innovation Centre Fondazione Edmund Mach, San Michele all'Adige, Italy
| | - Renata Vieira da Mota
- Empresa de Pesquisa Agropecuária de Minas Gerais - EPAMIG, Núcleo Tecnológico Uva e Vinho, Caldas, Minas Gerais, Brazil
| | - Eduardo Purgatto
- Deptartment of Food Science and Experimental Nutrition/FORC - Food Research Center, University of São Paulo, São Paulo, Brazil
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Centre Fondazione Edmund Mach, San Michele all'Adige, Italy
- Center Agriculture Food Environment, University of Trento, Trento, Italy
| | - Panagiotis Arapitsas
- Department of Food Quality and Nutrition, Research and Innovation Centre Fondazione Edmund Mach, San Michele all'Adige, Italy
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13
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Verhoeven A, Giera M, Mayboroda OA. Scientific workflow managers in metabolomics: an overview. Analyst 2020; 145:3801-3808. [DOI: 10.1039/d0an00272k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Metabolomics workflows for data processing reproducibility and accelerated clinical deployment.
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Affiliation(s)
- Aswin Verhoeven
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Martin Giera
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Oleg A. Mayboroda
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
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14
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Del Carratore F, Schmidt K, Vinaixa M, Hollywood KA, Greenland-Bews C, Takano E, Rogers S, Breitling R. Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships. Anal Chem 2019; 91:12799-12807. [PMID: 31509381 DOI: 10.1021/acs.analchem.9b02354] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientists often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, and fragmentation patterns). IPA (integrated probabilistic annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub ( https://github.com/francescodc87/IPA ), together with the related extensive documentation.
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Affiliation(s)
- Francesco Del Carratore
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Kamila Schmidt
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Maria Vinaixa
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Katherine A Hollywood
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Caitlin Greenland-Bews
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Eriko Takano
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
| | - Simon Rogers
- School of Computing Science , University of Glasgow , Glasgow , G12 8RZ , U.K
| | - Rainer Breitling
- Manchester Institute of Biotechnology, Faculty of Science and Engineering , University of Manchester , Manchester , M1 7DN , U.K
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15
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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16
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Narduzzi L, Stanstrup J, Mattivi F, Franceschi P. The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2018; 35:2145-2157. [DOI: 10.1080/19440049.2018.1523572] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Luca Narduzzi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all’Adige, Italy
| | - Jan Stanstrup
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all’Adige, Italy
- Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Fulvio Mattivi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all’Adige, Italy
- Centre for Agriculture, Food and the Environment, University of Trento, San Michele all’Adige, Italy
| | - Pietro Franceschi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all’Adige, Italy
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17
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Abstract
This chapter describes a protocol for the analysis of the metabolomic fingerprint of wine by liquid chromatography-mass spectrometry. The straightforward, optimized sample preparation procedure is limited to a single-step dilution with water or acetonitrile. The separation of wine analytes is carried out by two columns with orthogonal selectivity, including both reversed-phase (C18) and hydrophilic interaction (HILIC) chromatography, while the detection is assured by a high-resolution quadrupole time-of-flight mass spectrometer operating in negative and positive electrospray ionization mode, in order to obtain four different chromatograms for each sample. This validated protocol, or parts of it, could be applied in several oenological topic experimental designs, including wine quality and wine authenticity.
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Affiliation(s)
- Panagiotis Arapitsas
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy.
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy
- Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy
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18
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Li B, Tang J, Yang Q, Li S, Cui X, Li Y, Chen Y, Xue W, Li X, Zhu F. NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res 2017; 45:W162-W170. [PMID: 28525573 PMCID: PMC5570188 DOI: 10.1093/nar/gkx449] [Citation(s) in RCA: 255] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 04/22/2017] [Accepted: 05/09/2017] [Indexed: 01/15/2023] Open
Abstract
Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.
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Affiliation(s)
- Bo Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Shuang Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Yinghong Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Xiaofeng Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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19
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Perez de Souza L, Naake T, Tohge T, Fernie AR. From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. Gigascience 2017; 6:1-20. [PMID: 28520864 PMCID: PMC5499862 DOI: 10.1093/gigascience/gix037] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 05/08/2017] [Accepted: 05/12/2017] [Indexed: 01/19/2023] Open
Abstract
The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.
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Affiliation(s)
- Leonardo Perez de Souza
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Thomas Naake
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Takayuki Tohge
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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20
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Li B, Tang J, Yang Q, Cui X, Li S, Chen S, Cao Q, Xue W, Chen N, Zhu F. Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis. Sci Rep 2016; 6:38881. [PMID: 27958387 PMCID: PMC5153651 DOI: 10.1038/srep38881] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/15/2016] [Indexed: 02/06/2023] Open
Abstract
In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.
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Affiliation(s)
- Bo Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Shuang Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Sijie Chen
- College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
| | - Quanxing Cao
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Na Chen
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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21
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Shahaf N, Rogachev I, Heinig U, Meir S, Malitsky S, Battat M, Wyner H, Zheng S, Wehrens R, Aharoni A. The WEIZMASS spectral library for high-confidence metabolite identification. Nat Commun 2016; 7:12423. [PMID: 27571918 PMCID: PMC5013563 DOI: 10.1038/ncomms12423] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 06/27/2016] [Indexed: 12/25/2022] Open
Abstract
Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date. Unambiguous metabolite annotation is a critical, yet problematic step, in mass spectrometry based metabolomics. Here, Shahaf et al. present WEIZMASS, a platform consisting of a diverse spectral library of more than 3500 plant metabolites and software to aid their identification in biological samples.
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Affiliation(s)
- Nir Shahaf
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel.,Institute of Plant Sciences, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, PO Box 12, Rehovot 76100, Israel.,Research and Innovation Centre, Fondazione E. Mach, San Michele all'Adige, 38010 Trento, Italy
| | - Ilana Rogachev
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Uwe Heinig
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Sagit Meir
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Sergey Malitsky
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Maor Battat
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Hilary Wyner
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Shuning Zheng
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
| | - Ron Wehrens
- Research and Innovation Centre, Fondazione E. Mach, San Michele all'Adige, 38010 Trento, Italy.,Wageningen University and Research, Droevendaalsesteeg 1, Wageningen 6708 PB, The Netherlands
| | - Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, PO Box 26, Rehovot 7610001, Israel
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22
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Wehrens R, Hageman JA, van Eeuwijk F, Kooke R, Flood PJ, Wijnker E, Keurentjes JJB, Lommen A, van Eekelen HDLM, Hall RD, Mumm R, de Vos RCH. Improved batch correction in untargeted MS-based metabolomics. Metabolomics 2016; 12:88. [PMID: 27073351 PMCID: PMC4796354 DOI: 10.1007/s11306-016-1015-8] [Citation(s) in RCA: 146] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 02/18/2016] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. OBJECTIVES This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. METHODS Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC-MS and GC-MS data sets of samples from Arabidopsis thaliana. RESULTS The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects-replacing them with very small numbers such as zero seems the worst of the approaches considered. CONCLUSION The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.
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Affiliation(s)
- Ron Wehrens
- Biometris, Wageningen UR, Wageningen, The Netherlands
- Bioscience, Wageningen UR, Wageningen, The Netherlands
| | | | | | - Rik Kooke
- Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
- Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands
| | - Pádraic J. Flood
- Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
- Horticulture and Production Physiology, Wageningen UR, Wageningen, The Netherlands
- Max Planck Institute For Plant Breeding Research, Cologne, Germany
| | - Erik Wijnker
- Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
- Developmental Biology, Hamburg University, Hamburg, Germany
| | | | - Arjen Lommen
- RIKILT, Wageningen UR, Wageningen, The Netherlands
| | | | - Robert D. Hall
- Bioscience, Wageningen UR, Wageningen, The Netherlands
- Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands
| | - Roland Mumm
- Bioscience, Wageningen UR, Wageningen, The Netherlands
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23
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Rocca-Serra P, Salek RM, Arita M, Correa E, Dayalan S, Gonzalez-Beltran A, Ebbels T, Goodacre R, Hastings J, Haug K, Koulman A, Nikolski M, Oresic M, Sansone SA, Schober D, Smith J, Steinbeck C, Viant MR, Neumann S. Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics 2016; 12:14. [PMID: 26612985 PMCID: PMC4648992 DOI: 10.1007/s11306-015-0879-3] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 07/29/2015] [Indexed: 12/14/2022]
Abstract
Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.
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Affiliation(s)
- Philippe Rocca-Serra
- Oxford e-Research Centre, University of Oxford, 7 Keble Road, Oxford, OX1 3QG UK
| | - Reza M. Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Masanori Arita
- National Institute of Genetics, Mishima, Shizuoka 411-8540 Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045 Japan
| | - Elon Correa
- University of Manchester, Centre for Endocrinology and Diabetes, Old St Mary’s Building, Hathersage Road, Manchester, M13 9WL UK
- School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Saravanan Dayalan
- Metabolomics Australia, The University of Melbourne, Parkville, VIC 3010 Australia
| | | | - Tim Ebbels
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington, London, SW7 2AZ UK
| | - Royston Goodacre
- School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Janna Hastings
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Albert Koulman
- MRC Human Nutrition Research, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL UK
| | - Macha Nikolski
- Bordeaux Bioinformatics Center, Université de Bordeaux, Bordeaux, France
- CNRS/LaBRI, Université de Bordeaux, Talence, France
| | | | | | - Daniel Schober
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - James Smith
- MRC Human Nutrition Research, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL UK
- Department of Applied Mathematics and Theoretical Physics, Cambridge Computational Biology Institute, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Mark R. Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
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24
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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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25
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Arapitsas P, Corte AD, Gika H, Narduzzi L, Mattivi F, Theodoridis G. Studying the effect of storage conditions on the metabolite content of red wine using HILIC LC-MS based metabolomics. Food Chem 2015; 197 Pt B:1331-40. [PMID: 26675875 DOI: 10.1016/j.foodchem.2015.09.084] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 09/16/2015] [Accepted: 09/22/2015] [Indexed: 10/23/2022]
Abstract
The main aim of this work was to develop an untargeted normal phase LC-MS method, starting from a targeted method already validated for the analysis of 135 polar metabolites. Since the LC instrument and column were the same, most of the chromatographic conditions remained identical, while the adaptations focused on maintaining the ionic strength of the eluents constant. The sample preparation was simplified and the effectiveness of LC-MS for long batches was evaluated, in order to record the maximum number of metabolites with good chromatographic resolution and the best MS stability and accuracy. The method was applied to study the influence of storage conditions on wine composition. Slightly sub-optimum storage conditions had a major impact on the polar metabolite fingerprint of the red wines analysed and the markers revealed included phenolics, vitamins and metabolites indentified in wine for the first time (4-amino-heptanedioic acid and its ethyl ester).
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Affiliation(s)
- Panagiotis Arapitsas
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy.
| | - Anna Della Corte
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
| | - Helen Gika
- Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Luca Narduzzi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
| | - Georgios Theodoridis
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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26
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Narduzzi L, Stanstrup J, Mattivi F. Comparing Wild American Grapes with Vitis vinifera: A Metabolomics Study of Grape Composition. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2015; 63:6823-6834. [PMID: 26158394 DOI: 10.1021/acs.jafc.5b01999] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We analyzed via untargeted UHPLC-ESI-Q-TOF-MS the metabolome of the berry tissues (skin, pulp, seeds) of some American Vitis species (Vitis cinerea, Vitis californica, Vitis arizonica), together with four interspecific hybrids, and seven Vitis vinifera cultivars, aiming to find differences in the metabolomes of the American Vitis sp. versus Vitis vinifera. Apart from the known differences, that is, more complex content of anthocyanins and stilbenoids in the American grapes, we observed higher procyanidin accumulation (tens to hundreds of times) in the vinifera skin and seeds in comparison to American berries, and we confirmed this result via phloroglucinolysis. In the American grapes considered, we did not detect the accumulation of pleasing aroma precursors (terpenoids, glycosides), whereas they are common in vinifera grapes. We also found accumulation of hydrolyzable tannins and their precursors in the skin of the wild American grapes, which has never been reported earlier in any of the species under investigation. Such information is needed to improve the design of new breeding programs, lowering the risk of retaining undesirable characteristics in the chemical phenotype of the offspring.
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Affiliation(s)
- Luca Narduzzi
- †Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
- §International Doctoral School in Bio-molecular Science, University of Trento, Via Sommarive 14, 38123 Povo-Trento, Italy
| | - Jan Stanstrup
- †Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
| | - Fulvio Mattivi
- †Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, Italy
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