Characterization of GMO or glyphosate effects on the composition of maize grain and maize-based diet for rat feeding

Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement

Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.

Characterization of two closely related citrus cultivars using UPLC-ESI-MS/MS-based widely targeted metabolomics

Citrus cultivars are widely spread worldwide, and some of them only differ by specific mutations along the genome. It is difficult to distinguish them by traditional morphological identification. To accurately identify such similar cultivars, the subtle differences between them must be detected. In this study, UPLC-ESI-MS/MS-based widely targeted metabolomics analysis was conducted to study the chemical differences between two closely related citrus cultivars, Citrus reticulata 'DHP' and C. reticulata 'BZH'. Totally 352 metabolites including 11 terpenoids, 35 alkaloids, 80 phenolic acids, 25 coumarins, 7 lignans, 184 flavonoids and 10 other compounds were detected and identified; Among them, 15 metabolites are unique to DHP and 16 metabolites are unique to BZH. Hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA) can be used to clearly discriminate between DHP and BZH. 93 metabolites including 36 down-regulated and 57 up-regulated are significantly different in DHP and BZH. They are mainly involved in the biosynthesis of flavonoids, flavones, flavonols, and isoflavonoids. In addition, the relative content levels of flavonoids, alkaloids, and terpenoids are much higher in the peel of DHP than that of BZH, the presence of which may correlate with the quality difference of the peels. The results reported herein indicate that metabolite analysis based on UPLC-ESI-MS/MS is an effective means of identifying cultivars with different genotypes, especially those that cannot be distinguished based on traditional identification methods.

The GMO90+ Project: Absence of Evidence for Biologically Meaningful Effects of Genetically Modified Maize-based Diets on Wistar Rats After 6-Months Feeding Comparative Trial

The GMO90+ project was designed to identify biomarkers of exposure or health effects in Wistar Han RCC rats exposed in their diet to 2 genetically modified plants (GMP) and assess additional information with the use of metabolomic and transcriptomic techniques. Rats were fed for 6-months with 8 maize-based diets at 33% that comprised either MON810 (11% and 33%) or NK603 grains (11% and 33% with or without glyphosate treatment) or their corresponding near-isogenic controls. Extensive chemical and targeted analyses undertaken to assess each diet demonstrated that they could be used for the feeding trial. Rats were necropsied after 3 and 6 months. Based on the Organization for Economic Cooperation and Development test guideline 408, the parameters tested showed a limited number of significant differences in pairwise comparisons, very few concerning GMP versus non-GMP. In such cases, no biological relevance could be established owing to the absence of difference in biologically linked variables, dose-response effects, or clinical disorders. No alteration of the reproduction function and kidney physiology was found. Metabolomics analyses on fluids (blood, urine) were performed after 3, 4.5, and 6 months. Transcriptomics analyses on organs (liver, kidney) were performed after 3 and 6 months. Again, among the significant differences in pairwise comparisons, no GMP effect was observed in contrast to that of maize variety and culture site. Indeed, based on transcriptomic and metabolomic data, we could differentiate MON- to NK-based diets. In conclusion, using this experimental design, no biomarkers of adverse health effect could be attributed to the consumption of GMP diets in comparison with the consumption of their near-isogenic non-GMP controls.

Utilization of GC-MS untargeted metabolomics to assess the delayed response of glufosinate treatment of transgenic herbicide resistant (HR) buffalo grasses (Stenotaphrum secundatum L.)

INTRODUCTION

Herbicide resistant (HR) buffalo grasses were genetically engineered to resist the non-selective herbicide, glufosinate in order to facilitate a modern, 'weeding program' which is highly effective in terms of minimizing costs and labor. The resistant trait was conferred by an insertion of the pat gene to allow for the production of the enzyme phosphinothricin acetyltransferase (PAT) to detoxify the glufosinate inhibitive effect. To date, there are only a few reports using metabolomics as well as molecular characterizations published for glufosinate-resistant crops with no reports on HR turfgrass. Therefore, for the first time, this study examines the metabolome of glufosinate-resistant buffalo grasses which not only will be useful to future growers but also the scientific community.

OBJECTIVE

A major aim of this present work is to characterize and evaluate the metabolic alterations which may arise from a genetic transformation of HR buffalo grasses by comprehensively using gas chromatography-mass spectrometry (GC-MS) based untargeted metabolomics.

METHODS

Eight-week old plants of 4 HR buffalo grasses, (93-1A, 93-2B, 93-3C and 93-5A) and 3 wild type varieties (WT 8-4A, WT 9-1B and WT 9-1B) were selected for physiological, molecular and metabolomics experiments. Plants were either sprayed with 1, 5, 10 and 15% v/v of glufosinate to evaluate the visual injuries or submerged in 5% v/v of glufosinate 3 days prior to a GC-MS based untargeted metabolomics analysis. In contrast, the control group was treated with distilled water. Leaves were extracted in 1:1 methanol:water and then analysed, using an in-house GC-MS untargeted workflow.

RESULTS

Results identified 199 metabolites with only 6 of them (cis-aconitic acid, allantoin, cellobiose, glyceric acid, maltose and octadecanoic acid) found to be statistically significant (p < 0.05) between the HR and wild type buffalo grass varieties compared to the control experiment. Among these metabolites, unusual accumulation of allantoin was prominent and was an unanticipated effect of the pat gene insertion. As expected, glufosinate treatment caused significant metabolic alterations in the sensitive wild type, with the up-regulation of several amino acids (e.g. phenylalanine and isoleucine) which was likely due to glufosinate-induced senescence. The aminoacyl-tRNA biosynthetic pathway was identified as the most significant enriched pathway as a result of glufosinate effects because a number of its intermediates were amino acids.

CONCLUSION

HR buffalo grasses were very similar to its wild type comparator based on a comprehensive GC-MS based untargeted metabolomics and therefore, should guarantee the safe use of these HR buffalo grasses. The current metabolomics analyses not only confirmed the effects of glufosinate to up-regulate free amino acid pools in the sensitive wild type but also several alterations in sugar, sugar phosphate and organic acid metabolism have been reported.

Rat feeding trials: A comprehensive assessment of contaminants in both genetically modified maize and resulting pellets

We analyzed a comprehensive set of contaminants in MON810 and NK603 genetically modified (GM) maize, and their non-GM counterparts, used in a rat feeding study (the GMO90 + project). Both the maize grains and the manufactured pellets were characterized. Only minor differences in contaminant levels between GM and corresponding non-GM harvests were evidenced. Fumonisin and deoxynivalenol mycotoxins were the pollutants present in the highest amounts, with concentrations that were however largely below acceptance reference values. Our data reporting slightly lower levels of fumonisin in MON810 compared to its non-GM counterpart corroborate the lower susceptibility of insect resistant Bt maize to fumonisin-producing fungi. Traces of glyphosate (0.016 mg/kg) were evidenced in grains from NK603 treated crops. Regarding the pellets, analysis of more than 650 potentially toxic substances revealed low amounts of various mycotoxins, pesticides and heavy metals. Concentrations of contaminants quantified in the pellets were however far below the maximum level of residues values set by regulatory agencies, and no substantial differences in contaminants between GM and non-GM pellets were observed. Moreover, when comparing the contamination status of grains and pellets, we demonstrate yet again that characterizing the grains is actually not sufficient to foresee the quality of the produced pellets.