Advances in Plant Metabolomics

代谢组学 代谢组 计算生物学 蛋白质组 蛋白质组学 生物 系统生物学 生物技术 数据科学 生化工程 计算机科学 生物信息学 工程类 遗传学 基因
作者
Marta‐Marina Pérez‐Alonso,Víctor Carrasco‐Loba,Stephan Pollmann
出处
期刊: 卷期号:: 557-588 被引量:8
标识
DOI:10.1002/9781119312994.apr0660
摘要

Abstract Over recent years, metabolomics found its way into daily laboratory routine as an additional ‘omics’ platform technology, playing an increasingly important role in modern plant sciences. It is often complementing other approaches such as transcriptomics or proteomics in systems biology experiments. Representing the end products of biochemical processes, metabolites can be regarded as the ultimate readout of cellular regulatory processes, giving account on adaptations or changes of biological systems challenged by environmental or developmental stimuli. In analogy to the terms transcriptome and proteome, the entire set of metabolites produced by a biological system is referred to as its metabolome. Due to their extensive secondary metabolism, plants possess a metabolome extremely rich in small molecule metabolites. This makes metabolomics in plant sciences a particularly challenging task. In general terms, metabolomics refers to the systematic and comprehensive investigation of the greatest possible part of low molecular weight molecules in a biological sample. In first place, this is achieved by the unbiased assessment of mass spectrometric (MS) data. Metabolomics is a highly relevant method to improve our knowledge on alterations of biological processes in response to external and internal cues. Substantial advances in instrument technology promoted the advent of metabolomics approaches over the last 15 years. Future studies and further improvements in the field can be expected to provide an even deeper insight into the regulatory and biochemical intricacies in plants that will likely pave the way to novel breeding strategies and a more sustainable agriculture. This article strives to provide an overview of the state‐of‐the‐art in the research field, summarising the main MS‐based approaches that are commonly used to perform targeted and untargeted metabolomics experiments. Moreover, possible pitfalls and future trends will be discussed.

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