Oh Seon-Woo, Imran Muhammad, Kim Eun-Ha, Park Soo-Yun, Lee Sang-Gu, Park Hyoun-Min, Jung Jung-Won, Ryu Tae-Hun
Division of Biosafety, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Jeollabuk-do, Republic of Korea.
Front Plant Sci. 2023 Aug 11;14:1192235. doi: 10.3389/fpls.2023.1192235. eCollection 2023.
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.
代谢组学是指对生物系统（如细胞或组织）中的代谢物和低分子量化合物进行综合分析的技术。代谢物通过直接参与生理机制的调节，如维持细胞稳态或通过蛋白质-蛋白质相互作用进行信号传递，在生物现象中发挥重要作用。本综述旨在提供一个框架，说明如何通过对代谢物、其功能作用和内在生物学信息的综合分析来理解与代谢物调节相关的生物现象，以及如何将这些信息应用于对利用生物技术培育的作物的安全性评估。技术和分析仪器的进步带来了检验生物学与化学之间融合的新方法，从而对复杂的生物现象有了更深入的理解。代谢组学可通过使用代谢物水平数据处理算法、统计技术和数据库开发的系统方法，用于生物技术产品的安全性评估。将代谢组学数据与测序数据整合是朝着提供更多表型证据以阐明与代谢过程相关的基因组中发现的变异的环境影响程度迈出的关键一步。此外，必须引入大数据、机器学习和信息技术投资等信息分析技术，以建立一个数据提取、选择和代谢组学数据分析系统，用于解释生物技术创新的生物学意义。本综述概述了代谢组学评估在确定植物基因工程和生物技术后果方面的完整性。
