We obtained a new hybrid soybean (Hybrid) by hybridizing β-carotene-enhanced soybean (BCE; Glycine max L.) containing the phytoene synthase-2A-carotene desaturase gene and wild-type soybean (Wild; Glycine soja). To investigate metabolic changes between variants, we performed metabolic profiling of leaves (three growth stages) and seeds. Multivariate analyses revealed significant metabolic differences between genotypes in seeds and leaves, with seeds showing accumulation of phytosterols, tocopherols, and carotenoids (BCE only), indicating co-induction of the methylerythritol 4-phosphate and mevalonic acid pathways. Additionally, Hybrid produced intermediate levels of carotenoids and high levels of amino acids. Principal component analysis revealed metabolic discrimination between growth stages of soybean leaves and identified differences in leaf groups according to different genotypes at 8, 12, and 16 weeks, with Wild showing higher levels of environmental stress-related compounds relative to BCE and Hybrid leaves. The metabolic profiling approach could be a useful tool to identify metabolic links in various soybean cultivars.

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.

No abstract