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Co-expression network analyses identify functional modules associated with development and stress response in Gossypium arboreum

文献类型： 外文期刊

作者： You, Qi 1 ; Zhang, Liwei 1 ; Yi, Xin 1 ; Zhang, Kang 1 ; Yao, Dongxia 1 ; Zhang, Xueyan 1 ; Wang, Qianhua 1 ; Zhao, Xinhua 1 ; Ling, Yi 1 ; Xu, Wenying 1 ; Li, Fuguang 1 ; Su, Zhen 1 ;

作者机构： 1.State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China

2.State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agriculture Sciences (CAAS), Anyang, Henan 455000, China

期刊名称：Scientific Reports （ 影响因子：4.379； 五年影响因子：5.133 ）

ISSN： 2045-2322

年卷期： 2016 年 6 卷

页码：

收录情况： SCI

摘要： Cotton is an economically important crop, essential for the agriculture and textile industries. Through integrating transcriptomic data, we discovered that multi-dimensional co-expression network analysis was powerful for predicting cotton gene functions and functional modules. Here, the recently available transcriptomic data on Gossypium arboreum, including data on multiple growth stages of tissues and stress treatment samples were applied to construct a co-expression network exploring multi-dimensional expression (development and stress) through multi-layered approaches. Based on differential gene expression and network analysis, a fibre development regulatory module of the gene GaKNL1 was found to regulate the second cell wall through repressing the activity of REVOLUTA, and a tissue-selective module of GaJAZ1 alpha was examined in response to water stress. Moreover, comparative genomics analysis of the JAZ1-related regulatory module revealed high conservation across plant species. In addition, 1155 functional modules were identified through integrating the co-expression network, module classification and function enrichment tools, which cover functions such as metabolism, stress responses, and transcriptional regulation. In the end, an online platform was built for network analysis (http://structuralbiology. cau. edu. cn/arboreum), which could help to refine the annotation of cotton gene function and establish a data mining system to identify functional genes or modules with important agronomic traits.

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