Recent developments in metabolomics-based research in understanding transgenic grass metabolism

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

LC-MS untargeted metabolomics assesses the delayed response of glufosinate treatment of transgenic glufosinate resistant (GR) buffalo grasses (Stenotaphrum secundatum L.)

INTRODUCTION

Glufosinate resistant (GR) buffalo grasses were genetically modified to resist the broad-spectrum herbicide, glufosinate by inserting a novel pat gene into its genome. This modification results in a production of additional phosphinothricin acetyltransferase (PAT) to detoxify the deleterious effects of glufosinate. The GR grasses and its associated herbicide form a modern, weeding program, to eradicate obnoxious weeds in turf lawn without damaging the grasses at relatively low costs and labor. As with several principal crops which are genetically modified to improve agricultural traits, biosafety of the GR buffalo grasses is inevitably expected to become a public concern. For the first time, we had previously examined the metabolome of glufosinate-resistant buffalo grasses, using a GC-MS untargeted approach to assess the risk of GR as well as identify any pleotropic effects arising from the genetically modification process. In this paper, an untargeted high-resolution LC-MS (LC-HRMS) untargeted metabolomics approach was carried out to complement our previous findings with respect to GR and wild type (WT) buffalo grasses.

OBJECTIVE

One of the major aims of this present work was to compare GR to WT buffalo grasses by including the detection of the secondary metabolome and determine any unprecedented metabolic changes.

METHODS

Eight-week old plants of 4 GR buffalo grasses, (93-1A, 93-2B, 93-3 C and 93-5A) and 3 wild type varieties (WT 8-4A, WT 9-1B and WT 9-1B) were submerged in either 5 % v/v of glufosinate or distilled water 3 days prior to a LC-HRMS based untargeted metabolomics analysis (glufosinate-treated or control, samples, respectively). An Ultra-High-Performance Liquid Chromatography (UHPLC) system coupled to a Velos Pro Orbitrap mass spectrometer system was employed to holistically measure the primary and secondary metabolome of both GR and WT buffalo grasses either treated with or without glufosinate and subsequently apply several bioinformatic tools including the automated pathway analysis algorithm, mummichog.

RESULTS

LC-HRMS untargeted based metabolomics clearly identified that the global metabolite pools of both GR and WT cultivars were highly similar, providing strong, supporting evidence of substantial equivalence between the GR and WT varieties. These findings indicate that if any associated risks to these GR grasses were somehow present, the risk would be within those acceptable ranges present in the WT. Additionally, mummichog-based pathway analysis indicated that phenylalanine metabolism and the TCA cycle were significantly impacted by glufosinate treatment in the WT cultivar. It was possible that alterations in the relative concentrations of several intermediates in these pathways were likely due to glufosinate-induced production of secondary metabolites to enhance plant defense mechanisms against herbicidal stress at the expense of primary metabolism.

CONCLUSIONS

GR buffalo grasses were found to be near identical to its WT comparator based on this complementary LC-HRMS based untargeted metabolomics. Therefore, these results further support the safe use of these GR buffalo grasses with substantial evidence. Interestingly, despite protected by PAT, GR buffalo grasses still demonstrated the response to glufosinate treatment by up-regulating some secondary metabolite-related pathways.

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.

Long-Term Exposure of Psoralen and Isopsoralen Induced Hepatotoxicity and Serum Metabolites Profiles Changes in Female Rats

Pre-clinical safety evaluation of traditional medicines is imperative because of the universality of drug-induced adverse reactions. Psoralen and isopsoralen are the major active molecules and quality-control components of a traditional herbal medicine which is popularly used in Asia, Fructus Psoraleae. The purpose of this study is to assess the long-term effects of psoralen and isopsoralen with low levels on the biochemical parameters and metabolic profiles of rats. Three doses (14, 28, and 56 mg/kg) of psoralen and one dose (28 mg/kg) of isopsoralen were administered to rats over 12 weeks. Blood and selected tissue samples were collected and analyzed for hematology, serum biochemistry, and histopathology. Metabolic changes in serum samples were detected via proton nuclear magnetic resonance (¹H-NMR) spectroscopy. We found that psoralen significantly changed the visceral coefficients, blood biochemical parameters, and histopathology, and isopsoralen extra influenced the hematological index. Moreover, psoralen induced remarkable elevations of forvaline, isoleucine, isobutyrate, alanine, acetone, pyruvate, glutamine, citrate, unsaturated lipids, choline, creatine, phenylalanine, and 4-hydroxybenzoate, and significant reductions of ethanol and dimethyl sulfone. Isopsoralen only induced a few remarkable changes of metabolites. These results suggest that chronic exposure to low-level of psoralen causes a disturbance in alanine metabolism, glutamate metabolism, urea cycle, glucose-alanine cycle, ammonia recycling, glycine, and serine metabolism pathways. Psoralen and isopsoralen showed different toxicity characteristics to the rats.