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Oh SW, Imran M, Kim EH, Park SY, Lee SG, Park HM, Jung JW, Ryu TH. Approach strategies and application of metabolomics to biotechnology in plants. FRONTIERS IN PLANT SCIENCE 2023; 14:1192235. [PMID: 37636096 PMCID: PMC10451086 DOI: 10.3389/fpls.2023.1192235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.
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Brini A, Avagyan V, de Vos RCH, Vossen JH, van den Heuvel ER, Engel J. Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage. Metabolites 2021; 11:metabo11040237. [PMID: 33924479 PMCID: PMC8069634 DOI: 10.3390/metabo11040237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 01/15/2023] Open
Abstract
One-class modelling is a useful approach in metabolomics for the untargeted detection of abnormal metabolite profiles, when information from a set of reference observations is available to model "normal" or baseline metabolite profiles. Such outlying profiles are typically identified by comparing the distance between an observation and the reference class to a critical limit. Often, multivariate distance measures such as the Mahalanobis distance (MD) or principal component-based measures are used. These approaches, however, are either not applicable to untargeted metabolomics data, or their results are unreliable. In this paper, five distance measures for one-class modeling in untargeted metabolites are proposed. They are based on a combination of the MD and five so-called eigenvalue-shrinkage estimators of the covariance matrix of the reference class. A simple cross-validation procedure is proposed to set the critical limit for outlier detection. Simulation studies are used to identify which distance measure provides the best performance for one-class modeling, in terms of type I error and power to identify abnormal metabolite profiles. Empirical evidence demonstrates that this method has better type I error (false positive rate) and improved outlier detection power than the standard (principal component-based) one-class models. The method is illustrated by its application to liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic response spectroscopy (NMR) untargeted metabolomics data from two studies on food safety assessment and diagnosis of rare diseases, respectively.
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Affiliation(s)
- Alberto Brini
- Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands;
- Correspondence:
| | - Vahe Avagyan
- Biometris, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands; (V.A.); (J.E.)
| | - Ric C. H. de Vos
- Bioscience, Wageningen University and Research, Droevendaalsesteeg 1, 6700 AA Wageningen, The Netherlands;
| | - Jack H. Vossen
- Plant Breeding, Wageningen University and Research, Droevendaalsesteeg 1, 6700 AJ Wageningen, The Netherlands;
| | - Edwin R. van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands;
| | - Jasper Engel
- Biometris, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands; (V.A.); (J.E.)
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Bedair M, Glenn KC. Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops. Metabolomics 2020; 16:111. [PMID: 33037482 PMCID: PMC7547035 DOI: 10.1007/s11306-020-01733-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 09/29/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND The safety assessment of foods and feeds from genetically modified (GM) crops includes the comparison of key characteristics, such as crop composition, agronomic phenotype and observations from animal feeding studies compared to conventional counterpart varieties that have a history of safe consumption, often including a near isogenic variety. The comparative compositional analysis of GM crops has been based on targeted, validated, quantitative analytical methods for the key food and feed nutrients and antinutrients for each crop, as identified by Organization of Economic Co-operation and Development (OCED). As technologies for untargeted metabolomic methods have evolved, proposals have emerged for their use to complement or replace targeted compositional analytical methods in regulatory risk assessments of GM crops to increase the number of analyzed metabolites. AIM OF REVIEW The technical opportunities, challenges and strategies of including untargeted metabolomics analysis in the comparative safety assessment of GM crops are reviewed. The results from metabolomics studies of GM and conventional crops published over the last eight years provide context to enable the discussion of whether metabolomics can materially improve the risk assessment of food and feed from GM crops beyond that possible by the Codex-defined practices used worldwide for more than 25 years. KEY SCIENTIFIC CONCEPTS OF REVIEW Published studies to date show that environmental and genetic factors affect plant metabolomics profiles. In contrast, the plant biotechnology process used to make GM crops has little, if any consequence, unless the inserted GM trait is intended to alter food or feed composition. The nutritional value and safety of food and feed from GM crops is well informed by the quantitative, validated compositional methods for list of key analytes defined by crop-specific OECD consensus documents. Untargeted metabolic profiling has yet to provide data that better informs the safety assessment of GM crops than the already rigorous Codex-defined quantitative comparative assessment. Furthermore, technical challenges limit the implementation of untargeted metabolomics for regulatory purposes: no single extraction method or analytical technique captures the complete plant metabolome; a large percentage of metabolites features are unknown, requiring additional research to understand if differences for such unknowns affect food/feed safety; and standardized methods are needed to provide reproducible data over time and laboratories.
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van der Voet H, Paoletti C. Equivalence Testing Approaches in Genetically Modified Organism Risk Assessment. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:13506-13508. [PMID: 31725270 PMCID: PMC6909263 DOI: 10.1021/acs.jafc.9b05149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 10/09/2019] [Accepted: 11/14/2019] [Indexed: 06/10/2023]
Abstract
Since 2011, the European Food Safety Authority (EFSA) has implemented combined difference and equivalence testing of agronomic, phenotypic, and composition data in the risk assessment of genetically modified crops. A short perspective is provided on misunderstandings that have shown up in published criticisms of the approach to equivalence testing, different viewpoints regarding the questions to be answered, and new developments in statistical modeling.
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Corujo M, Pla M, van Dijk J, Voorhuijzen M, Staats M, Slot M, Lommen A, Barros E, Nadal A, Puigdomènech P, Paz JLL, van der Voet H, Kok E. Use of omics analytical methods in the study of genetically modified maize varieties tested in 90 days feeding trials. Food Chem 2019; 292:359-371. [DOI: 10.1016/j.foodchem.2018.05.109] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 05/04/2018] [Accepted: 05/24/2018] [Indexed: 10/16/2022]
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Lanzoni A, Castoldi AF, Kass GE, Terron A, De Seze G, Bal-Price A, Bois FY, Delclos KB, Doerge DR, Fritsche E, Halldorsson T, Kolossa-Gehring M, Hougaard Bennekou S, Koning F, Lampen A, Leist M, Mantus E, Rousselle C, Siegrist M, Steinberg P, Tritscher A, Van de Water B, Vineis P, Walker N, Wallace H, Whelan M, Younes M. Advancing human health risk assessment. EFSA J 2019; 17:e170712. [PMID: 32626449 PMCID: PMC7015480 DOI: 10.2903/j.efsa.2019.e170712] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The current/traditional human health risk assessment paradigm is challenged by recent scientific and technical advances, and ethical demands. The current approach is considered too resource intensive, is not always reliable, can raise issues of reproducibility, is mostly animal based and does not necessarily provide an understanding of the underlying mechanisms of toxicity. From an ethical and scientific viewpoint, a paradigm shift is required to deliver testing strategies that enable reliable, animal-free hazard and risk assessments, which are based on a mechanistic understanding of chemical toxicity and make use of exposure science and epidemiological data. This shift will require a new philosophy, new data, multidisciplinary expertise and more flexible regulations. Re-engineering of available data is also deemed necessary as data should be accessible, readable, interpretable and usable. Dedicated training to build the capacity in terms of expertise is necessary, together with practical resources allocated to education. The dialogue between risk assessors, risk managers, academia and stakeholders should be promoted further to understand scientific and societal needs. Genuine interest in taking risk assessment forward should drive the change and should be supported by flexible funding. This publication builds upon presentations made and discussions held during the break-out session 'Advancing risk assessment science - Human health' at EFSA's third Scientific Conference 'Science, Food and Society' (Parma, Italy, 18-21 September 2018).
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Affiliation(s)
| | | | | | | | | | | | - Frédéric Y Bois
- French National Institute for Industrial Environment and Risks FR
| | - K Barry Delclos
- National Center for Toxicological Research US Food and Drug Administration USA
| | - Daniel R Doerge
- National Center for Toxicological Research US Food and Drug Administration USA
| | - Ellen Fritsche
- Leibniz Research Institute for Environmental Medicine DE
| | | | | | | | | | | | | | - Ellen Mantus
- The National Academies of Sciences, Engineering, and Medicine USA
| | | | | | | | | | - Bob Van de Water
- Drug Discovery and Safety Leiden Academic Centre for Drug Research Leiden University NL
| | | | - Nigel Walker
- National Toxicology Program/National Institute of Environmental Health Sciences USA
| | - Heather Wallace
- Institute of Medical Sciences University of Aberdeen Scotland UK
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Christ B, Pluskal T, Aubry S, Weng JK. Contribution of Untargeted Metabolomics for Future Assessment of Biotech Crops. TRENDS IN PLANT SCIENCE 2018; 23:1047-1056. [PMID: 30361071 DOI: 10.1016/j.tplants.2018.09.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/14/2018] [Accepted: 09/24/2018] [Indexed: 05/20/2023]
Abstract
The nutritional value and safety of food crops are ultimately determined by their chemical composition. Recent developments in the field of metabolomics have made it possible to characterize the metabolic profile of crops in a comprehensive and high-throughput manner. Here, we propose that state-of-the-art untargeted metabolomics technology should be leveraged for safety assessment of new crop products. We suggest generally applicable experimental design principles that facilitate the efficient and rigorous identification of both intended and unintended metabolic alterations associated with a newly engineered trait. Our proposition could contribute to increased transparency of the safety assessment process for new biotech crops.
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Affiliation(s)
- Bastien Christ
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Tomáš Pluskal
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Sylvain Aubry
- Federal Office for Agriculture, 3003 Bern, Switzerland; Department of Plant and Microbial Biology, University of Zurich, 8008 Zurich, Switzerland.
| | - Jing-Ke Weng
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Aguilera J, Aguilera‐Gomez M, Barrucci F, Cocconcelli PS, Davies H, Denslow N, Lou Dorne J, Grohmann L, Herman L, Hogstrand C, Kass GEN, Kille P, Kleter G, Nogué F, Plant NJ, Ramon M, Schoonjans R, Waigmann E, Wright MC. EFSA Scientific Colloquium 24 – 'omics in risk assessment: state of the art and next steps. ACTA ACUST UNITED AC 2018. [DOI: 10.2903/sp.efsa.2018.en-1512] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | - Lutz Grohmann
- Federal Office of Consumer Protection and Food Safety
| | | | | | | | | | | | - Fabien Nogué
- French National Institute for Agricultural Research INRA
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Comments on two recent publications on GM maize and Roundup. Sci Rep 2018; 8:13338. [PMID: 30177715 PMCID: PMC6120907 DOI: 10.1038/s41598-018-30440-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/08/2018] [Indexed: 11/24/2022] Open
Abstract
Two -omics studies on genetically modified maize and Roundup-fed rats, recently published in the journal Scientific Reports, contain serious flaws in the experimental design, methodology and interpretation of results, which we point out here. The use of -omics technologies are of increasing importance in research, however we argue for a cautious approach to the potential application in food safety assessments as these exceptionally sensitive and complex methods require a thorough and detailed evaluation of the biological significance of obtained results. Arising from: Mesnage et al. Sci Rep 7:39328 (2017), Mesnage et al. Sci Rep 6:37855 (2016).
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Unintended Effects in Genetically Modified Food/Feed Safety: A Way Forward. Trends Biotechnol 2018; 36:872-875. [DOI: 10.1016/j.tibtech.2018.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 03/20/2018] [Accepted: 03/21/2018] [Indexed: 11/22/2022]
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Kok E, van Dijk J, Voorhuijzen M, Staats M, Slot M, Lommen A, Venema D, Pla M, Corujo M, Barros E, Hutten R, Jansen J, van der Voet H. Omics analyses of potato plant materials using an improved one-class classification tool to identify aberrant compositional profiles in risk assessment procedures. Food Chem 2018; 292:350-358. [PMID: 31054687 DOI: 10.1016/j.foodchem.2018.07.224] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 07/24/2018] [Accepted: 07/31/2018] [Indexed: 10/28/2022]
Abstract
The objective of this study was to quantitatively assess potato omics profiles of new varieties for meaningful differences from analogous profiles of commercial varieties through the SIMCA one-class classification model. Analytical profiles of nine commercial potato varieties, eleven experimental potato varieties, one GM potato variety that had acquired Phytophtora resistance based on a single insert with potato-derived DNA sequences, and its non-GM commercial counterpart were generated. The ten conventional varieties were used to construct the one-class model. Omics profiles from experimental non-GM and GM varieties were assessed using the one-class SIMCA models. No potential unintended effects were identified in the case of the GM variety. The model showed that varieties that were genetically more distant from the commercial varieties were recognized as aberrant, highlighting its potential in determining whether additional evaluation is required for the risk assessment of materials produced from any breeding technique, including genetic modification.
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Affiliation(s)
- Esther Kok
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands.
| | - Jeroen van Dijk
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Marleen Voorhuijzen
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Martijn Staats
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Martijn Slot
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Arjen Lommen
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Dini Venema
- RIKILT Wageningen University & Research, Akkermaalsbos 2, 6708 WB Wageningen, the Netherlands
| | - Maria Pla
- University of Girona, Institute for Food and Agricultural Technology (INTEA), Campus Montilivi (EPS-1), 17003 Girona, Spain
| | - Maria Corujo
- Centre for Research in Agricultural Genomics (CRAG), Edifici CRAG, Campus UAB, 08193 Cerdanyola, Barcelona, Spain
| | - Eugenia Barros
- Council for Scientific and Industrial Research (CSIR), Biosciences, Brummeria, Pretoria, South Africa
| | - Ronald Hutten
- Wageningen University & Research, Plant Breeding, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands
| | - Jeroen Jansen
- University of Nijmegen, Comeniuslaan 4, 6525 HP Nijmegen, the Netherlands
| | - Hilko van der Voet
- Wageningen University & Research, Biometris, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands
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van der Voet H. Safety Assessments and Multiplicity Adjustment: Comments on a Recent Paper. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2018; 66:2194-2195. [PMID: 29455520 PMCID: PMC5843949 DOI: 10.1021/acs.jafc.7b03686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Indexed: 06/08/2023]
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Çakir Ö, Meriç S, Meriç S, Ari Ş. GMO Analysis Methods for Food: From Today to Tomorrow. Food Saf (Tokyo) 2016. [DOI: 10.1002/9781119160588.ch5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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de Mello CS, Van Dijk JP, Voorhuijzen M, Kok EJ, Arisi ACM. Tuber proteome comparison of five potato varieties by principal component analysis. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2016; 96:3928-3936. [PMID: 26799786 DOI: 10.1002/jsfa.7635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 01/08/2016] [Accepted: 01/11/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND Data analysis of omics data should be performed by multivariate analysis such as principal component analysis (PCA). The way data are clustered in PCA is of major importance to develop some classification systems based on multivariate analysis, such as soft independent modeling of class analogy (SIMCA). In a previous study a one-class classifier based on SIMCA was built using microarray data from a set of potatoes. The PCA grouped the transcriptomic data according to varieties. The present work aimed to use PCA to verify the clustering of the proteomic profiles for the same potato varieties. RESULTS Proteomic profiles of five potato varieties (Biogold, Fontane, Innovator, Lady Rosetta and Maris Piper) were evaluated by two-dimensional gel electrophoresis (2-DE) performed on two immobilized pH gradient (IPG) strip lengths, 13 and 24 cm, both under pH range 4-7. For each strip length, two gels were prepared from each variety; in total there were ten gels per analysis. For 13 cm strips, 199-320 spots were detected per gel, and for 24 cm strips, 365-684 spots. CONCLUSION All four PCAs performed with these datasets presented clear grouping of samples according to the varieties. The data presented here showed that PCA was applicable for proteomic analysis of potato and was able to separate the samples by varieties. © 2016 Society of Chemical Industry.
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Affiliation(s)
- Carla Souza de Mello
- Food Science and Technology Department, Federal University of Santa Catarina, Rod. Admar Gonzaga 1346, 88034-001, Florianópolis, SC, Brazil
| | - Jeroen P Van Dijk
- RIKILT, Wageningen University and Research Centre, PO Box 230, NL-6700, AE, Wageningen, The Netherlands
| | - Marleen Voorhuijzen
- RIKILT, Wageningen University and Research Centre, PO Box 230, NL-6700, AE, Wageningen, The Netherlands
| | - Esther J Kok
- RIKILT, Wageningen University and Research Centre, PO Box 230, NL-6700, AE, Wageningen, The Netherlands
| | - Ana Carolina Maisonnave Arisi
- Food Science and Technology Department, Federal University of Santa Catarina, Rod. Admar Gonzaga 1346, 88034-001, Florianópolis, SC, Brazil
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Valentim-Neto PA, Rossi GB, Anacleto KB, de Mello CS, Balsamo GM, Arisi ACM. Leaf proteome comparison of two GM common bean varieties and their non-GM counterparts by principal component analysis. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2016; 96:927-932. [PMID: 25760408 DOI: 10.1002/jsfa.7166] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 02/02/2015] [Accepted: 03/07/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND A genetically modified (GM) common bean event, namely Embrapa 5.1, was approved for commercialization in Brazil. The present work aimed to use principal component analysis (PCA) to compare the proteomic profile of this GM common bean and its non-GM counterpart. RESULTS Seedlings from four Brazilian common bean varieties were grown under controlled environmental conditions. Leaf proteomic profiles were analyzed by two-dimensional gel electrophoresis (2DE). First, a comparison among 12 gels from four common bean varieties was performed by PCA using volume percentage of 198 matched spots, presented in all gels. The first two principal components (PC) accounted for 46.8% of total variation. Two groups were clearly separated by the first component: Pérola and GM Pérola from Pontal and GM Pontal. Secondly, another comparison among six gels from the same variety GM and its non-GM counterpart was performed by PCA; in this case it was possible to distinguish GM and non-GM. CONCLUSION Separation between leaf proteomic profile of GM common bean variety and its counterpart was observed only when they were compared in pairs. These results showed higher similarity between GM variety and its counterpart than between two common bean varieties. PCA is a useful tool to compare proteomes of GM and non-GM plant varieties.
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Affiliation(s)
- Pedro A Valentim-Neto
- Food Science and Technology Department, Federal University of Santa Catarina, 88034-001, Florianópolis, SC, Brazil
| | - Gabriela B Rossi
- Food Science and Technology Department, Federal University of Santa Catarina, 88034-001, Florianópolis, SC, Brazil
| | - Kelly B Anacleto
- Food Science and Technology Department, Federal University of Santa Catarina, 88034-001, Florianópolis, SC, Brazil
| | - Carla S de Mello
- Food Science and Technology Department, Federal University of Santa Catarina, 88034-001, Florianópolis, SC, Brazil
| | - Geisi M Balsamo
- Food Science and Technology Department, Federal University of Santa Catarina, 88034-001, Florianópolis, SC, Brazil
| | - Ana Carolina M Arisi
- Food Science and Technology Department, Federal University of Santa Catarina, 88034-001, Florianópolis, SC, Brazil
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16
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Balsamo GM, Valentim-Neto PA, Mello CS, Arisi ACM. Comparative Proteomic Analysis of Two Varieties of Genetically Modified (GM) Embrapa 5.1 Common Bean (Phaseolus vulgaris L.) and Their Non-GM Counterparts. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2015; 63:10569-10577. [PMID: 26575080 DOI: 10.1021/acs.jafc.5b04659] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The genetically modified (GM) common bean event Embrapa 5.1 was commercially approved in Brazil in 2011; it is resistant to golden mosaic virus infection. In the present work grain proteome profiles of two Embrapa 5.1 common bean varieties, Pérola and Pontal, and their non-GM counterparts were compared by two-dimensional gel electrophoresis (2-DE) followed by mass spectrometry (MS). Analyses detected 23 spots differentially accumulated between GM Pérola and non-GM Pérola and 21 spots between GM Pontal and non-GM Pontal, although they were not the same proteins in Pérola and Pontal varieties, indicating that the variability observed may not be due to the genetic transformation. Among them, eight proteins were identified in Pérola varieties, and four proteins were identified in Pontal. Moreover, we applied principal component analysis (PCA) on 2-DE data, and variation between varieties was explained in the first two principal components. This work provides a first 2-DE-MS/MS-based analysis of Embrapa 5.1 common bean grains.
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Affiliation(s)
- Geisi M Balsamo
- Food Science and Technology Department, Federal University of Santa Catarina , Rod. Admar Gonzaga 1346, 88034-001 Florianópolis, Santa Catarina, Brazil
| | - Pedro A Valentim-Neto
- Food Science and Technology Department, Federal University of Santa Catarina , Rod. Admar Gonzaga 1346, 88034-001 Florianópolis, Santa Catarina, Brazil
| | - Carla S Mello
- Food Science and Technology Department, Federal University of Santa Catarina , Rod. Admar Gonzaga 1346, 88034-001 Florianópolis, Santa Catarina, Brazil
| | - Ana C M Arisi
- Food Science and Technology Department, Federal University of Santa Catarina , Rod. Admar Gonzaga 1346, 88034-001 Florianópolis, Santa Catarina, Brazil
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Ladics GS, Bartholomaeus A, Bregitzer P, Doerrer NG, Gray A, Holzhauser T, Jordan M, Keese P, Kok E, Macdonald P, Parrott W, Privalle L, Raybould A, Rhee SY, Rice E, Romeis J, Vaughn J, Wal JM, Glenn K. Genetic basis and detection of unintended effects in genetically modified crop plants. Transgenic Res 2015; 24:587-603. [PMID: 25716164 PMCID: PMC4504983 DOI: 10.1007/s11248-015-9867-7] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2015] [Accepted: 02/14/2015] [Indexed: 11/26/2022]
Abstract
In January 2014, an international meeting sponsored by the International Life Sciences Institute/Health and Environmental Sciences Institute and the Canadian Food Inspection Agency titled “Genetic Basis of Unintended Effects in Modified Plants” was held in Ottawa, Canada, bringing together over 75 scientists from academia, government, and the agro-biotech industry. The objectives of the meeting were to explore current knowledge and identify areas requiring further study on unintended effects in plants and to discuss how this information can inform and improve genetically modified (GM) crop risk assessments. The meeting featured presentations on the molecular basis of plant genome variability in general, unintended changes at the molecular and phenotypic levels, and the development and use of hypothesis-driven evaluations of unintended effects in assessing conventional and GM crops. The development and role of emerging “omics” technologies in the assessment of unintended effects was also discussed. Several themes recurred in a number of talks; for example, a common observation was that no system for genetic modification, including conventional methods of plant breeding, is without unintended effects. Another common observation was that “unintended” does not necessarily mean “harmful”. This paper summarizes key points from the information presented at the meeting to provide readers with current viewpoints on these topics.
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Affiliation(s)
- Gregory S. Ladics
- DuPont Pioneer Agricultural Biotechnology, DuPont Experimental Station, 200 Powder Mill Road, Wilmington, DE 19803 USA
| | - Andrew Bartholomaeus
- Therapeutics Research Centre, School of Medicine, Queensland University, Brisbane, QLD 4072 Australia
- Faculty of Health, School of Pharmacy, University of Canberra, Locked Bag 1, Canberra, ACT 2601 Australia
| | - Phil Bregitzer
- National Small Grains Germplasm Research Facility, US Department of Agriculture – Agricultural Research Service, 1691 S. 2700 W., Aberdeen, ID 83210 USA
| | - Nancy G. Doerrer
- ILSI Health and Environmental Sciences Institute, 1156 15th St., NW, Suite 200, Washington, DC 20005 USA
| | - Alan Gray
- Centre for Ecology and Hydrology, CEH Wallingford, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB UK
| | - Thomas Holzhauser
- Division of Allergology, Paul-Ehrlich-Institut, Paul-Ehrlich-Strasse 51-59, 63225 Langen, Germany
| | - Mark Jordan
- Cereal Research Centre, Agriculture and Agri-Food Canada, 101 Route 100, Morden, MB R6M 1Y5 Canada
| | - Paul Keese
- Office of the Gene Technology Regulator, Australian Government, MDP54, GPO Box 9848, Canberra, ACT 2601 Australia
| | - Esther Kok
- RIKILT Wageningen UR, P.O. Box 230, 6700 AE Wageningen, The Netherlands
| | - Phil Macdonald
- Canadian Food Inspection Agency, 1400 Merivale Rd, Ottawa, ON K1A 0Y9 Canada
| | - Wayne Parrott
- Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, GA 30602 USA
| | - Laura Privalle
- Bayer CropScience, 407 Davis Drive, Morrisville, NC 27560 USA
| | - Alan Raybould
- Syngenta Ltd, Jealott’s Hill International Research Centre, Bracknell, RG42 6EY UK
- Present Address: Syngenta Crop Protection AG, Schwarzwaldallee 215, 4058 Basel, Switzerland
| | - Seung Yon Rhee
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama St., Stanford, CA 94305 USA
| | - Elena Rice
- Monsanto Company, 700 Chesterfield Pkwy W., CC5A, Chesterfield, MO 63017 USA
| | - Jörg Romeis
- Agroscope, Institute for Sustainability Sciences ISS, Reckenholzstr. 191, 8046 Zurich, Switzerland
| | - Justin Vaughn
- University of Georgia, 111 Riverbend Road, Athens, GA 30602 USA
| | - Jean-Michel Wal
- Dept. SVS, AgroParisTech, 16 rue Claude Bernard, 75231 Paris, France
| | - Kevin Glenn
- Monsanto Company, 800 N. Lindbergh Blvd, U4NA, St. Louis, MO 63167 USA
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