Contribution of Untargeted Metabolomics
for Future Assessment of Biotech Crops
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Citation
Bastien, Christ et al. "Contribution of Untargeted Metabolomics for
Future Assessment of Biotech Crops." Trends in Plant Science 23,
12 (December 2018): P1047-1056 © 2018 Elsevier Ltd
As Published
http://dx.doi.org/10.1016/j.tplants.2018.09.011
Publisher
Elsevier BV
Version
Author's final manuscript
Citable link
https://hdl.handle.net/1721.1/126266
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Opinion paper in Trends in Plant Science
Contribution of untargeted metabolomics for future assessment of
biotech crops
Bastien Christ1, Tomáš Pluskal1, Sylvain Aubry2,3* and Jing-Ke Weng1,4*
1 Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA 2 Federal Office for Agriculture, 3003 Bern, Switzerland
3 Department of Plant and Microbial Biology, University of Zurich, 8008, Zurich, Switzerland 4 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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 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.
A new risk assessment framework is needed for next-generation biotech crops
New genetic engineering tools like CRISPR/Cas systems generate novel genetically engineered (GE) crops harboring genomic modifications without the necessity of stably introducing foreign transgenes [1]. As a consequence, the current framework of risk assessment (RA) of GE crops needs an update to keep up with these recent advances in plant biotechnology [2–4]. Historically, RA of GE products relies on the identification and subsequent evaluation of the direct effects of a particular GE trait in a new crop product otherwise deemed substantially equivalent to its parental background [5–9]. The premise is that the safe history of the parental crop could be extended to the new crop, wherein assessment should be focused on the added components [10–12].
Whereas policy decisions regarding GE crops vary significantly among different countries, general guidelines and recommendations for scientific RA have been provided by international organizations such as the Codex Alimentarius, EFSA and the OECD consensus documents (see [6,13–15] for more details). In practice, the current assessment approach is not designed to detect unintended effects that may arise from the breeding process or genetic engineering, and therefore has been previously criticized [16,17]. Ways to improve or reform this framework have also been proposed [5,11,13,18–20]. For example, the assessment of substantial equivalence based on a restricted set of metabolites might not be sufficient to guarantee the safety of the new biotech crop [21]. A recent study reporting unintended production of metabolites in transgenic crops containing one of the most popular GE traits, BAR, which confers resistance to the herbicide glufosinate [22], led us to propose that an untargeted assessment approach should be adopted for future evaluation of GE crops. Although high-throughput omics techniques have become mainstream in both basic and translational research [23–26], they have not been routinely applied during standardized RA procedures for biotech crops. Here, we advocate the
inclusion of untargeted metabolomics, an omics methodology directly impinging on metabolic phenotypes [27], as a routine protocol for assessing future biotech crops.
Benefits of untargeted metabolomics for GE crop risk assessment
Untargeted metabolomics is a high-throughput analytical chemistry technique that measures all detectable metabolites in a given sample. In general, it is applied for two primary research purposes: metabolite fingerprinting and metabolite profiling [28,29]. Metabolite fingerprinting is intended for rapid classification of samples based on overall patterns of measured metabolite signals, regardless of their identification. On the other hand, metabolite profiling quantifies individual metabolite features separately, therefore allowing the identification of metabolites that display statistically different levels among sample groups. In recent years, the field of metabolomics has witnessed significant advances in both instrumentation and software development, rendering extraordinary capacity for researchers to capture a significant fraction of the metabolome of complex biological systems in a high-throughput manner [28]. The application of untargeted metabolomics has benefited many areas in basic research and biotechnology, such as disease diagnostics [30,31], functional genomics [32–34], and precision plant breeding (BOX 1) [8,35–40].
The notion that crop safety assessment should focus more attention on the metabolome has long been suggested [13,37,41–43]. Ultimately, when consumed by animals and humans, the metabolites present in the edible parts of a crop contribute greatly to the nutritional value as well as potential risk factors. For instance, crop domestication has led to significant reduction in the concentration of numerous toxic specialized metabolites, such as steroidal glycoalkaloids in solanaceous vegetables [44], and glucosinolates and erucic acid in rapeseed [44,45]. Implementing state-of-the-art untargeted metabolomics could reveal both intended and unintended metabolic changes resulted from conventional breeding and/or genetic engineering in the new biotech crop relative to its predecessor [42]. Here, unintended metabolic changes are
defined as statistically significant differences in metabolite features between the newly developed crop and its parent lines, which are not readily predictable based on the rationale of a particular engineering approach. These effects may or may not be a concern for food safety, but should be taken into consideration during the evaluation process.
Numerous studies have illustrated the utility of untargeted metabolomics to improve GE crop safety assessment [16,46–48]. For example, Catchpole et al. used untargeted metabolomics to compare the metabolomes of GE and conventional potatoes [16]. Using both a hierarchical approach based on metabolite fingerprinting and a more detailed metabolite profiling approach, conventional and GE germplasms were shown to be substantially similar in composition [16]. Shepherd et al. profiled the metabolomes of GE potato lines engineered to reduce the accumulation of glycoalkaloids and revealed that, although phytosterol-derived glycoalkaloid levels were reduced as expected, levels of phytosterols such as β-sitosterol increased [46]. Furthermore, little variation between GE and control lines was observed for most detectable metabolite features [46]. Two other untargeted metabolomic studies of GE wheat and barley varieties report that genetic manipulations have less effects on the crop metabolome compared to those caused by other factors, such as growth conditions and genetic differences between conventional cultivars [47,48].
The recently reported unintended metabolic consequences of the BAR gene expressed in transgenic crops is another illustration of the utility of untargeted metabolomics in assessing new GE traits in crops [22]. BAR encodes a bacterial acetyltransferase that has N-acetylation activity towards the herbicidal amino acid phosphinothricin. BAR is widely used in basic research and agriculture to engineer herbicide resistance in transgenic plants. Christ et al. reported that BAR, which was assumed to be specific to its native substrate phosphinothricin [49,50], shows significant levels of promiscuity in planta by acetylating tryptophan and the lysine degradation product aminoadipate [22]. This discovery has broad implications for the development of new GE crops expressing non-native enzymes, or native enzymes with genetically altered expression
levels or catalytic activity. Enzymes should be considered not as perfect catalysts but as potentially promiscuous ones, at both the design and the RA steps. Although the newly discovered acetyl-aminoadipate and acetyl-tryptophan present in BAR-containing crops were recently assessed by the U. S. Food and Drug Administration (FDA) as safe for human and animal consumption at the level measured in these GE crops [51], the case of BAR should be considered as a cautionary tale.
General guidelines and challenges for implementing untargeted metabolomics in biotech crop assessment
The field of metabolomics has established a set of general guidelines for experimental design and reporting standards for untargeted metabolomic studies [52,53]. However, it is not immediately clear how these guidelines should be properly implemented for the purpose of GE crop assessment. Effective guidelines should be generally applicable to a wide range of analytical platforms, and promote the production of high-quality datasets that can be validated and utilized by different labs. Community effort on data accessibility and common tool building in the field of untargeted metabolomics will also improve the assessment process for biotech crops. This section briefly reviews each step of the untargeted metabolomic pipeline with considerations on several critical technical aspects and challenges for its implementation in biotech crop assessment.
Choice of comparators, growth conditions, and sample preparation
Selecting proper genotypes for comparison, known as conventional counterparts, is a prerequisite for good experimental design [9]. If available, isogenic lines should be used as the background in the case of vegetatively propagated crops to identify metabolic alterations resulted only from the specific genetic manipulation in question. Ideally, plants should be cultivated under field conditions at multiple geographic locations and, if applicable, with the treatment (e.g.
herbicide) designed for the new trait [14]. In some cases, however, it is not feasible to cultivate conventional counterparts under the same conditions where the GE lines are designed to grow (e.g. crops containing traits conferring resistance to stresses or herbicides) [15], which inevitably compromises the full capacity of assessment by untargeted metabolomics. Comparative analysis involving multiple non-GE varieties may also yield insights into metabolic variations arising from “natural” genetic diversity and further help to define metabolic changes solely resulting from genetic engineering [38].
Artifactual modifications of the metabolome should be minimized during sample collection, storage, and preparation prior to analysis. Enzymatic and non-enzymatic reactions, which may occur rapidly after sample collection, can be reduced by quenching samples in liquid nitrogen and by proper long-term storage at -80°C. The chemodiversity of plants is enormous and spans a wide range of chemical properties [54–57]. Therefore, careful consideration should be given to the development of suitable extraction and/or fractionation protocols, such as solid-phase and liquid-liquid extractions that enrich certain classes of metabolites. If a large portion of the metabolome must be covered, multiple extraction and/or fractionation methods can be performed. The method development step should consider the nature of the GE trait. For instance, to evaluate a trait involving a transgenic acetyltransferase that exhibits activity on amino acids (e.g. BAR), sample preparation methods that preferentially enrich methanol-soluble, small, acetylated metabolites should be considered [22].
Analytical platforms for metabolomics
Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the two most commonly used detection methods for untargeted metabolomics. Total metabolite extracts of a biological sample can be directly analyzed without separation (e.g., NMR and direct infusion MS), or separated chromatographically before detection (e.g., liquid chromatography–MS (LC–MS), gas chromatography–MS (GC–MS) and LC–NMR) [28]. MS measures mass-to-charge ratios of
precursor and fragment ions derived from metabolites. NMR identifies and quantifies metabolites based on the observed resonant frequencies of nuclei within a magnetic field. NMR is often applied for metabolic fingerprinting, owing to the ease of sample preparation and the ability to capture global metabolic profiles without the need for derivatization or ionization [58]. In contrast, MS combined with chromatographic separation offers superior sensitivity and thus is more suitable for RA of crops [59]. However, in most cases, the exact chemical structures of unknown compounds cannot be determined solely using MS data, and are typically solved together with NMR (BOX 2).
Analysis and interpretation of untargeted metabolomic data
High-resolution MS and NMR platforms generate multidimensional spectral datasets, which require several stages of processing before they can be interpreted [60]. MS data pre-processing starts with feature detection using open-source (e.g., XCMS [61,62], MZmine [63], MetAlign [64], OpenMS [65], and others) or proprietary software packages. In the case of NMR, although open-source tools are available (e.g., rNMR [66]), commercial softwares are more widely used. While each tool uses its own set of algorithms, reproducible and reliable results can be obtained with most of the mainstream tools, providing that parameters for each step are properly optimized [67]. The resulting metabolite features are further aligned, identified and quantified across multiple samples. Additional data pre-processing steps may also include noise filtering, chromatogram baseline correction, retention time normalization, smoothing, and scaling [68].
Metabolic alterations between sample groups are typically identified using univariate and multivariate methods. Univariate methods such as Welch's t-test (pairwise analysis) and ANOVA (multigroup analysis) are used to find significantly dysregulated metabolite features. Numerous multivariate statistical methods allow exploration and visualization of metabolomic datasets through simultaneous analysis of multiple variables [69–71]. In practice, the choice of statistical methods should be made as part of the experimental design process [9,72]. For example,
clustering of sample groups can be achieved using unsupervised principal component analysis (PCA) or supervised partial least squares (PLS) methods [69]. These approaches have been previously used to compare GE varieties with a set of conventional cultivars based on metabolite fingerprints [16,72]. Univariate methods are well suited for analyzing metabolite profiling data and precisely identifying dysregulated metabolic features within specific classes of metabolites. For crop assessment, specific classification tools can be further developed using machine learning approaches. Van Dijk et al. proposed an innovative transcriptomics-based one-class classifier method for selecting crop variants based on global pattern of gene expression [73]. Similar approach could be applied to metabolomic datasets to identify new crop varieties that show significant metabolic variations compared to reference baseline classes of conventional cultivars.
A multi-tiered framework for RA using untargeted metabolomic data
The integration of high-throughput omics techniques into the RA procedure for crops is non-trivial. It is difficult to balance between strategies focusing on a set of relatively restrictive parameters that may miss hazards and strategies that would include large amounts of irrelevant data [41,74]. Guided by the nature of the new trait, the aim of our progressive approach is to collect a proportionate set of untargeted metabolomic data to inform an efficient RA process (Figure 1). Comparative analysis of this nature is used during voluntary consultation processes with the U.S. FDA, but has mainly been carried out using targeted metabolomic analyses [75]. Similarly, multi-tiered untargeted metabolomics could be implemented based on the guidelines for RA of GE crops defined by the European Food Safety Authority (EFSA) [9]. The comprehensiveness of each layer of the multi-tiered approach is dictated by the level of residual risk acceptable within specific socio-economic contexts.
Our proposed progressive framework starts with a comprehensive analysis of the new trait on a case-by-case basis. This step does not focus on the actual process used to introduce the specific trait, but rather on the intended and unintended alterations in the metabolome (if any).
New genetic engineering tools expand the types of genetic or epigenetic modifications that can be introduced to crops. When focusing on metabolic modifications, new traits can be readily divided into two categories: (1) those that are primarily designed to modify the metabolic capabilities of the organism and (2) those that are not, or with an unknown mode of action in the host. For example, direct modification of the plant metabolome, e.g. a change in fatty acid content or starch composition via modifications of endogenous metabolic enzymatic activities, would fall into the first category. Traits of the first category can also be enzymes not present in the initial parent that are designed to confer new metabolic capability of the crop, e.g. the ectopic expression of a bacterial enzyme conferring herbicide tolerance. The second category includes traits such as expression of insecticidal proteins, RNA interference to confer resistance to viruses, or expression of transcription factors with a reasonable probability of having unpredictable effects on the host metabolome. On one hand, in-depth study of information available on the trait and metabolic pathways will help define intended metabolic modifications of trait belonging to the first category. On the other hand, the relevant biological information of a new trait can also help predict unintended effects on other metabolites related by structural features and/or pathway context.
The metabolomic analysis of this proposed framework begins with an initial untargeted metabolite fingerprinting, as suggested previously [16]. Metabolite fingerprinting covers a large fraction of the whole metabolome and, therefore, can help identify potential intended and unintended effects in an unbiased manner. Based on this initial screen and, if applicable, with the biological information regarding the new trait, a second, more refined, metabolite profiling is then performed on selected classes of metabolites, allowing more sensitive and quantitative analysis.
Statistical analysis of data resulted from metabolite fingerprinting and profiling may reveal whether the new trait induces significant metabolic alterations or dysregulation. For that purpose, the quality and exhaustivity of data obtained from conventional counterparts is key to define natural variability of endogenous metabolomes. If identified, dysregulated or newly emerged metabolites should be structurally characterized (BOX 2). At this stage, an initial risk
characterization can be performed based on the identity and relative abundance of the compounds. If necessary, further in-depth hazard characterization (allergenicity, chronic and acute toxicity) and exposure assessment can be carried out, focusing on a small set of dysregulated and/or new metabolites [9,14].
An efficient framework would greatly benefit from the contribution of standardized and validated metabolomic datasets from researchers and crop developers to public databases. An increasing body of data produced for RA of GE crops will contribute to a better contextualization of observed metabolic variations in each individual user-specific case.
Concluding remarks and perspective
Historically, domestication and breeding have led to large-scale genome duplication, rearrangement, and mutagenesis events to the ancestral crop genomes that produced the variety of crops we consume today (BOX 1). New GE technologies, from Agrobacterium-mediated T-DNA insertions to the more recently developed genome-editing technologies, allow scientists to further improve crops by introducing desirable traits more quickly and more precisely. The lack of social acceptance of GE technologies is largely caused by general public concerns about whether modern breeders can fully understand the complexity of the new phenotypes arising from various GE approaches and the potential risks associated with them. The metabolite-centered framework presented here aims primarily at improving the existing RA approach to address the ever-growing complexity of biotech crops both in the techniques used (e.g. multiplexed gene editing, epigenetic modifications, etc.) and in the traits developed [76].
Statistical analysis and structural characterization of metabolites are the two major bottlenecks of MS-based untargeted metabolomics. Due to its high sensitivity, these analytical platforms provide large datasets composed of complex information that remain difficult to analyze using existing tools. Therefore, advanced tool development is much needed to fully harness the
power of MS-based untargeted metabolomics. The comprehensive information gained from these datasets will not only be useful for risk assessment, but also guide breeding process.
The current state-of-the-art MS instruments and sample preparation protocols can capture a large fraction of the metabolome. Nevertheless, MS-based metabolomics will likely never be able to cover the entire metabolome. Furthermore, GE technologies can potentially affect others classes of biomolecules such as proteins and RNAs, which are typically not monitored by metabolomics. Our metabolite-centered framework can be amended by other high-throughput omics techniques such as proteomics and transcriptomics. We believe that the study of GE crops using a multi-omics approach will greatly expedite the deregulation process through a comprehensive understanding of potential changes in all major classes of biomolecules, and, thus, lead to more transparency that may help improve public acceptance of GE traits in crops.
The intention of this framework is not to impose more regulatory hurdles to the developers of new crops, but to help establish a more efficient and standardized protocol to facilitate RA. One additional advantage of a more comprehensive a priori characterization of biotech crops using untargeted metabolomics may also a reduced demand for animal feed trials during the RA procedure [77]. Many factors ultimately affect whether state-of-the-art, high-throughput techniques, like untargeted metabolomics, will be implemented in future RA process for biotech crops, but gaining more knowledge and transparency about our food products using new technologies will always be a good thing.
Box 1. Metabolomics-assisted breeding
Crop domestication and breeding led to the selection of desirable phenotypes according to human needs. Among all phenotypic changes, alteration of metabolic traits are directly related to the nutritional value and risk factor of crop foods [78]. However, the extent to which human-guided selection has modified crop metabolism is not well understood. Using metabolomics, a recent study reported that significant metabolic changes are associated with the domestication of several tetraploid wheat varieties (Triticum turgidum L.) [79]. Whole-genome association studies (GWAS) on metabolic traits (mGWAS) is another powerful approach to establish genetic basis of metabolic variants in crops (reviewed in [39]). Using multi-omics data, specific mQTLs (metabolite quantitative trait loci) have been identified in tomato, relating to fruit size, taste and colour, as well as anti-nutrient levels [24,80,81]. In the case of rice, 840 metabolite features have been identified in 524 rice accessions, providing an important resource of well-defined metabolite features to guide future breeding efforts [82]. For many crops [40], metabolomics-assisted breeding together with other omics techniques will greatly improve the precision and efficiency of future breeding process, especially for selecting new desirable metabolic traits [83–85].
Box 2. Metabolite identification
Untargeted metabolomics in combination with statistical analyses can reveal up- and down-regulated metabolite features in a particular sample group relative to control(s). However, identification of the chemical structures corresponding to these features is a non-trivial task [86,87]. In cases where annotation at the level of chemical formulas is sufficient, the molecular formula of the analyte can be determined from accurate mass measurement and isotope abundance ratios [88]. For assigning chemical structures, the latest trend in the field is to integrate spectral database search with in silico prediction methods based on fragmentation analysis and/or
machine learning [89]. However, the ultimate confirmation of the true identity of the analyte still often relies on in-depth NMR and/or crystallography methods.
MS-Based Spectral Databases
A number of publicly accessible spectral databases can be used for mass spectrum similarity search [90]. The two largest databases, MoNA (http://mona.fiehnlab.ucdavis.edu) and METLIN [91], contain tens of thousands of verified experimental mass spectra. In addition, the Global Natural Products Social Molecular Networking (GNPS) database allows uploading and sharing of unidentified spectra, and thus enables community-driven identification of new metabolites by mass spectral matching [92]. A common problem with database spectrum matching is the estimation of confidence scores. This has been recently addressed by designing false discovery rate (FDR) metrics using artificially constructed decoy databases [93]. Although over 200,000 plant metabolites are known to date [57], only a small fraction of these metabolites (if detected) can be annotated using databases. In the context of crop metabolomics, more exhaustive reference datasets are yet to be established, particularly for main commodity crops.
In silico spectral interpretation of MS data
In silico prediction algorithms, such as MetFrag [94], CFM-ID [95], MS2LDA [96], CSI-FingerID [97], and others, are designed to predict the most likely chemical structure that corresponds to a given experimental mass spectrum using information obtained from known compounds stored in chemical databases, such as PubChem [98]. Given no other information than the mass spectrum, the best machine learning-based methods can currently identify the correct structure in about 30% of tested cases, while reporting the correct structure among the top 10 candidates in about 50% of the cases [99]. Although these methods can be very helpful in identifying compounds absent in mass spectral databases, additional effort is necessary to integrate these algorithms into common metabolomics data processing packages to facilitate routine analysis [100].
Experimental structure elucidation
If the chemical identity of a metabolite feature cannot be confidently assigned using spectral databases or in silico methods, and such assignment is required for safety or legal reasons, the unknown metabolite must be purified for structural determination by NMR and/or crystallographic analysis. As a rule of thumb, conventional structure elucidation by NMR requires several milligrams of purified material. This quantity requirement can be reduced about 20-fold by the application of low-volume cryo probes [101]. Recently, a cutting-edge approach based on crystalline sponge X-ray diffraction can further achieve absolute structure of compounds at nanogram quantity, sometimes even from crude extracts [102,103].
Glossary
Biotech crop: cultivated plant containing trait(s) derived from genetic engineering.
Conventional counterparts: non-GE germplasms genetically closely related to a GE variety.
Genetically engineered (GE) organism: organism with its genetic material intentionally modified using biotechnology.
Metabolites: small molecules that are biosynthesized, processed and degraded in cellular metabolism. Metabolite fingerprinting: high-throughput classification of samples based on global patterns of measured metabolite signals regardless of their identification.
Metabolite-genome wide association study (mGWAS): Large-scale association studies aiming at linking variations in metabolic phenotypes with genetic variations.
Metabolite profiling: quantification of a large group of metabolite features belonging to a selected class of compounds.
Mass spectrometry (MS): analytical chemistry technique to identify and quantify metabolites by measuring the mass-to-charge ratio of their ionized chemical species.
Isogenic line: germplasm that is genetically identical to a GE line except for the newly engineered trait. Nuclear magnetic resonance (NMR) spectroscopy: analytical chemistry technique to identify and quantify metabolites based on the observation of resonant frequencies of nuclei within a magnetic field. Omics: techniques that exhaustively analyze an entire class of biological molecules, such as genomics, transcriptomics and metabolomics.
Risk assessment (RA): quantitative or qualitative evaluation of risk factors of a crop food product. Specialized metabolites: metabolites distributed taxonomically in living organisms, which are not immediately required for survival but contribute to host fitness in environmental niches.
Substantial equivalence: analysis comparing GE crops and their conventional counterparts. It has been used historically as a starting point for RA of GE crops.
Untargeted metabolomics: comprehensive analysis of a large fraction of the entire metabolome of a biological sample using metabolite fingerprinting and/or profiling techniques.
Highlights
- The metabolome of food crops ultimately dictates their quality and safety.
- An updated framework for biotech crop risk assessment is needed in light of the rapid emergence of new genetic engineering tools for altering crop traits.
- Recent advances in the field of metabolomics enable comprehensive characterization of plant metabolomes in a high-throughput manner.
- Implementation of state-of-the-art untargeted metabolomics technologies could improve the safety assessment process for future biotech crops.
Outstanding Questions Box
- How does crop risk assessment keep up with the flourishing traits made possible by new breeding and genetic engineering technologies?
- Should crops developed through conventional methods, such as chemical or radiation-induced mutagenesis, be subjected to assessment using untargeted metabolomics approach?
- What new information do we gain from untargeted metabolomic analysis that helps the crop risk assessment process?
- How can one tease apart ectopic metabolic alterations caused by genetic engineering from the natural variations of metabolome in crop plants?
Figure 1: A proposed framework to integrate untargeted metabolomic analysis in biotech crops risk assessment
This multi-tiered framework is subdivided into four main stages. An initial collection of the available biological information regarding a new trait allows an assessor to predict potential effects on the crop metabolome (if any). An exhaustive set of references representative of the existing genetic diversity of a crop species is included in the study design to estimate natural metabolic diversity within the species. Information collected in the first stage is used to guide the choice of a dedicated protocol of untargeted metabolomic analysis (metabolite fingerprinting and/or profiling). Statistical analysis of the metabolomic data and partial identification of metabolite features lead to an initial characterization of the potential risk linked to the identified metabolic alterations. This initial risk characterization can then trigger further in-depth hazard characterization and exposure assessment. ZFNs, zinc finger nucleases; TALENs, transcription activator-like effector nucleases; CRISPR, clustered regularly interspaced palindromic repeats.
Keywords
Untargeted metabolomics, Risk assessment, Genetically engineered crops, Breeding.
Acknowledgements
We thank Stefan Hörtensteiner and Enrico Martinoia for helpful discussions. This work was supported by the Swiss National Science Foundation (postdoctoral fellowship P300PA_167641 to B.C.), the Helen Hay Whitney Foundation (postdoctoral fellowship F1055 to T.P.), the Pew Scholar Program in the Biomedical Sciences (J.K.W.) and the Searle Scholars Program (J.K.W.).
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policies or positions of the WIBR, MIT, UZH, or the FOAG
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