Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

代谢组学 物候学 生物 鉴定(生物学) 蛋白质组学 计算生物学 组学 基因组学 有机体 功能基因组学 表观遗传学 生物技术 生物信息学 数据科学 生态学 计算机科学 基因组 遗传学 基因表达 基因 DNA甲基化
作者
Shuangqian Shen,Chuansong Zhan,Chenkun Yang,Alisdair R. Fernie,Jie Luo
出处
期刊:Molecular Plant [Elsevier BV]
卷期号:16 (1): 43-63 被引量:111
标识
DOI:10.1016/j.molp.2022.09.007
摘要

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.
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