Integrated omics analysis of specialized metabolism in medicinal plants
Integrated omics analysis of specialized metabolism in medicinal plants
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DOI：
10.1111/tpj.13485
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发表时间：
2017-05-01
期刊：
影响因子：
7.2
通讯作者：
Yamazaki, Mami
中科院分区：
文献类型：
--
作者：
Rai, Amit;Saito, Kazuki;Yamazaki, Mami
药用植物是具有重要药理学特性的高度多样化的专门代谢物的丰富来源。直到最近，植物生物学家在探索这些代谢物的生物合成途径方面的能力有限，主要是由于植物基因组学资源的稀缺。然而，高通量大规模分析方法的最新进展使植物生物学家能够发现重要的植物药用代谢物的生物合成途径。生成组学数据集的成本降低以及用于其分析和整合的计算工具的开发导致了对植物来源的几种生物活性代谢物的生物合成途径的阐明。这些发现激发了合成生物学方法来开发微生物系统，以产生源自植物的生物活性代谢物，这是一种替代的可持续的药用重要化学品来源。由于对药用化合物的需求随着世界人口的增加而增加，因此了解特定代谢物的完整生物合成对于未来识别或开发可靠来源变得重要。在这里，我们回顾了主要的组学方法的贡献，以及它们对我们理解生物活性代谢物的生物合成途径的整合。我们简要地讨论了不同的方法集成组学数据集提取生物学相关的知识和组学数据集在代谢模型的构建和重建中的应用。
Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.