Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops

Assessment of the potential risks in SD rats gavaged with genetically modified yeast containing the cp4-epsps gene

Introduction

Despite the absence of definitive evidence indicating that the cp4-epsps gene and its resultant recombinant proteins have significant harmful effects on either human or animal health, the safety assessment of genetically modified (GM) crops expressing the CP4-EPSPS proteins has been controversial. This study endeavor was aimed at evaluating the potential risks posed by the CP4-EPSPS protein in transgenic crops, thereby contributing to the advancement of risk assessment methodologies in the context of genetically engineered crops.

Methods

To ascertain the appropriate daily dosages for oral gavage administration, the expression levels of the CP4-EPSPS protein in a recombinant yeast were quantified. Subsequently, physiological and biochemical analysis, metabolomics, and metagenomic analysis were conducted based on a 90-day Sprague-Dawley (SD) rats feeding experiment, respectively, thereby enhancing the depth and precision of our risk assessment framework.

Results

The results from the physiological and biochemical analysis, organ pathological, blood metabolism, gut microbiota, and correlation analysis of metabolites and gut microbiota revealed several biomarkers for further risk assessment. These biomarkers include clinical biochemical indexes such as total bilirubin (TBIL), direct bilirubin (DBIL), creatine kinase (CK), and lactate dehydrogenase (LDH); metabolites like Methionine, 2-Oxovaleric acid, and LysoPC (16:0); and gut microbiota including Blautia wexlerae, Holdemanella biformis, Dorea sp. CAG 317, Coriobacteriaceae and Erysipelotrichaceae.

Conclusion

In conclusion, the risk can be significantly reduced by directly consuming inactivated recombinant CP4-EPSPS. Therefore, in everyday life, the risk associated with consuming GM foods containing recombinant CP4-EPSPS is substantially reduced after heat treatment.

NMR Fingerprinting of Conventional and Genetically Modified Soybean Plants with AtAREB1 Transcription Factors

Drought stress impacts soybean yields and physiological processes. However, the insertion of the activated form of the AtAREB1 gene in the soybean cultivar BR16, which is sensitive to water deficit, improved the drought response of the genetically modified plants. Thus, in this study, we used 1H NMR in solution and solid-state NMR to investigate the response of genetically modified soybean overexpressing AtAREB1 under water deficiency conditions. We achieved that drought-tolerant soybean yields high content of amino acids isoleucine, leucine, threonine, valine, proline, glutamate, aspartate, asparagine, tyrosine, and phenylalanine after 12 days of drought stress conditions, as compared to drought-sensitive soybean under the same conditions. Specific target compounds, including sugars, organic acids, and phenolic compounds, were identified as involved in controlling sensitive soybean during the vegetative stage. Solid-state NMR was used to study the impact of drought stress on starch and cellulose contents in different soybean genotypes. The findings provide insights into the metabolic adjustments of soybean overexpressing AREB transcription factors in adapting to dry climates. This study presents NMR techniques for investigating the metabolome of transgenic soybean plants in response to the water deficit. The approach allowed for the identification of physiological and morphological changes in drought-resistant and drought-tolerant soybean tissues. The findings indicate that drought stress significantly alters micro- and macromolecular metabolism in soybean plants. Differential responses were observed among roots and leaves as well as drought-tolerant and drought-sensitive cultivars, highlighting the complex interplay between overexpressed transcription factors and drought stress in soybean plants.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

Approach strategies and application of metabolomics to biotechnology in plants

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.

Proteomic and Metabolomic Evaluation of Insect- and Herbicide-Resistant Maize Seeds

Label-free quantitative proteomic (LFQ) and widely targeted metabolomic analyses were applied in the safety evaluation of three genetically modified (GM) maize varieties, BBL, BFL-1, and BFL-2, in addition to their corresponding non-GM parent maize. A total of 76, 40, and 25 differentially expressed proteins (DEPs) were screened out in BBL, BFL-1, and BFL-2, respectively, and their abundance compared was with that in their non-GM parents. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that most of the DEPs participate in biosynthesis of secondary metabolites, biosynthesis of amino acids, and metabolic pathways. Metabolomic analyses revealed 145, 178, and 88 differentially accumulated metabolites (DAMs) in the BBL/ZH58, BFL-1/ZH58, and BFL-2/ZH58×CH72 comparisons, respectively. KEGG pathway enrichment analysis showed that most of the DAMs are involved in biosynthesis of amino acids, and in arginine and proline metabolism. Three co-DEPs and 11 co-DAMs were identified in the seeds of these GM maize lines. The proteomic profiling of seeds showed that the GM maize varieties were not dramatically different from their non-GM control. Similarly, the metabolomic profiling of seeds showed no dramatic changes in the GM/non-GM maize varieties compared with the GM/GM and non-GM/non-GM maize varieties. The genetic background of the transgenic maize was found to have some influence on its proteomic and metabolomic profiles.

Recent applications of metabolomics in plant breeding

Metabolites play a central role in maintaining organismal life and in defining crop phenotypes, such as nutritional value, fragrance, color, and stress resistance. Among the 'omes' in biology, the metabolome is the closest to the phenotype. Consequently, metabolomics has been applied to crop improvement as method for monitoring changes in chemical compositions, clarifying the mechanisms underlying cellular functions, discovering markers and diagnostics, and phenotyping for mQTL, mGWAS, and metabolite-genome predictions. In this review, 359 reports of the most recent applications of metabolomics to plant breeding-related studies were examined. In addition to the major crops, more than 160 other crops including rare medicinal plants were considered. One bottleneck associated with using metabolomics is the wide array of instruments that are used to obtain data and the ambiguity associated with metabolite identification and quantification. To further the application of metabolomics to plant breeding, the features and perspectives of the technology are discussed.

Rhizosphere Tripartite Interactions and PGPR-Mediated Metabolic Reprogramming towards ISR and Plant Priming: A Metabolomics Review

Plant growth-promoting rhizobacteria (PGPR) are beneficial microorganisms colonising the rhizosphere. PGPR are involved in plant growth promotion and plant priming against biotic and abiotic stresses. Plant-microbe interactions occur through chemical communications in the rhizosphere and a tripartite interaction mechanism between plants, pathogenic microbes and plant-beneficial microbes has been defined. However, comprehensive information on the rhizosphere communications between plants and microbes, the tripartite interactions and the biochemical implications of these interactions on the plant metabolome is minimal and not yet widely available nor well understood. Furthermore, the mechanistic nature of PGPR effects on induced systemic resistance (ISR) and priming in plants at the molecular and metabolic levels is yet to be fully elucidated. As such, research investigating chemical communication in the rhizosphere is currently underway. Over the past decades, metabolomics approaches have been extensively used in describing the detailed metabolome of organisms and have allowed the understanding of metabolic reprogramming in plants due to tripartite interactions. Here, we review communication systems between plants and microorganisms in the rhizosphere that lead to plant growth stimulation and priming/induced resistance and the applications of metabolomics in understanding these complex tripartite interactions.

Transparency in risk-disproportionate regulation of modern crop-breeding techniques

Despite over 25 years of safe deployment of genetically engineered crops, the number, complexity, and scope of regulatory studies required for global approvals continue to increase devoid of adequate scientific justification. Recently, there have been calls to further expand the scope of study and data requirements to improve public acceptance. However, increased regulation can actually generate consumer distrust due to the misperception that risks are high. We believe risk-disproportionate regulation as a means to advocate for acceptance of technology is counterproductive, even though some regulatory authorities believe it part of their mandate. To help avoid public distrust, the concept of regulatory transparency to demystify regulatory decision-making should be extended to clearly justifying specific regulatory requirements as: 1) risk-driven (i.e., proportionately addressing increased risk compared with traditional breeding), or 2) advocacy-driven (i.e., primarily addressing consumer concerns and acceptance). Such transparency in the motivation for requiring risk-disproportionate studies would: 1) lessen over-prescriptive regulation, 2) save public and private resources, 3) make beneficial products and technologies available to society sooner, 4) reduce needless animal sacrifice, 5) improve regulatory decision-making regarding safety, and 6) lessen public distrust that is generated by risk-disproportionate regulation.

Integrated proteomics and metabolomics analysis of transgenic and gene-stacked maize line seeds

Unintended effects of genetically modified (GM) crops may pose safety issues. Omics techniques provide researchers with useful tools to assess such unintended effects. Proteomics and metabolomics analyses were performed for three GM maize varieties, 2A-7, CC-2, and 2A-7×CC-2 stacked transgenic maize, and the corresponding non-GM parent Zheng58.Proteomics revealed 120, 271 and 135 maize differentially expressed proteins (DEPs) in the 2A-7/Zheng58, CC-2/Zheng58 and 2A-7×CC-2/Zheng58 comparisons, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that most DEPs participated in metabolic pathways and the biosynthesis of secondary metabolite. Metabolomics revealed 179, 135 and 131 differentially accumulated metabolites (DAMs) in the 2A-7/Zheng58, CC-2/Zheng58 and 2A-7×CC-2/Zheng58 comparisons, respectively. Based on KEGG enrichment analysis, most DAMs are involved in the biosynthesis of secondary metabolite and metabolic pathways. According to integrated proteomics and metabolomics analysis, the introduction of exogenous EPSPS did not affect the expression levels of six other enzymes or the abundance of seven metabolites involved in the shikimic acid pathway in CC-2 and 2A-7×CC-2 seeds. Six co-DEPs annotated by integrated proteomics and metabolomics pathway analysis were further analyzed by qRT-PCR.This study successfully employed integrated proteomic and metabolomic technology to assess unintended changes in maize varieties. The results suggest that GM and gene stacking do not cause significantly unintended effects.