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硫系统生物学——解析组学数据

Sulphur systems biology-making sense of omics data.

机构信息

Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.

Nara Institute of Science and Technology, Ikoma, Japan.

出版信息

J Exp Bot. 2019 Aug 19;70(16):4155-4170. doi: 10.1093/jxb/erz260.

Abstract

Systems biology approaches have been applied over the last two decades to study plant sulphur metabolism. These 'sulphur-omics' approaches have been developed in parallel with the advancing field of systems biology, which is characterized by permanent improvements of high-throughput methods to obtain system-wide data. The aim is to obtain a holistic view of sulphur metabolism and to generate models that allow predictions of metabolic and physiological responses. Besides known sulphur-responsive genes derived from previous studies, numerous genes have been identified in transcriptomics studies. This has not only increased our knowledge of sulphur metabolism but has also revealed links between metabolic processes, thus indicating a previously unexpected complex interconnectivity. The identification of response and control networks has been supported through metabolomics and proteomics studies. Due to the complex interlacing nature of biological processes, experimental validation using targeted or systems approaches is ongoing. There is still room for improvement in integrating the findings from studies of metabolomes, proteomes, and metabolic fluxes into a single unifying concept and to generate consistent models. We therefore suggest a joint effort of the sulphur research community to standardize data acquisition. Furthermore, focusing on a few different model plant systems would help overcome the problem of fragmented data, and would allow us to provide a standard data set against which future experiments can be designed and compared.

摘要

在过去的二十年中,系统生物学方法被应用于研究植物硫代谢。这些“硫组学”方法与系统生物学的不断发展平行发展,系统生物学的特点是高通量方法的不断改进,以获得全系统的数据。其目的是获得硫代谢的整体观点,并生成允许预测代谢和生理响应的模型。除了先前研究中得出的已知硫响应基因外,在转录组学研究中还鉴定了许多基因。这不仅增加了我们对硫代谢的了解,而且还揭示了代谢过程之间的联系,从而表明存在以前未预料到的复杂相互关联。通过代谢组学和蛋白质组学研究支持了响应和控制网络的鉴定。由于生物过程的复杂交织性质,使用靶向或系统方法进行的实验验证仍在进行中。将代谢组、蛋白质组和代谢通量研究的结果整合到一个统一的概念中并生成一致的模型仍有改进的空间。因此,我们建议硫研究界共同努力,规范数据采集。此外,专注于少数几种不同的模式植物系统将有助于克服数据碎片化的问题,并使我们能够提供一个标准数据集,以供将来设计和比较实验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f983/6698701/fd6b1bd6f1e6/erz260f0001a.jpg

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