Wong Darren C J, Matus José Tomás
Ecology and Evolution, Research School of Biology, Australian National UniversityActon, ACT, Australia.
Centre for Research in Agricultural Genomics, CSIC-IRTA-UAB-UBBarcelona, Spain.
Front Plant Sci. 2017 Apr 12;8:505. doi: 10.3389/fpls.2017.00505. eCollection 2017.
Representing large biological data as networks is becoming increasingly adopted for predicting gene function while elucidating the multifaceted organization of life processes. In grapevine ( L.), network analyses have been mostly adopted to contribute to the understanding of the regulatory mechanisms that control berry composition. Whereas, some studies have used gene co-expression networks to find common pathways and putative targets for transcription factors related to development and metabolism, others have defined networks of primary and secondary metabolites for characterizing the main metabolic differences between cultivars throughout fruit ripening. Lately, proteomic-related networks and those integrating genome-wide analyses of promoter regulatory elements have also been generated. The integration of all these data in multilayered networks allows building complex maps of molecular regulation and interaction. This perspective article describes the currently available network data and related resources for grapevine. With the aim of illustrating data integration approaches into network construction and analysis in grapevine, we searched for berry-specific regulators of the phenylpropanoid pathway. We generated a composite network consisting of overlaying maps of co-expression between structural and transcription factor genes, integrated with the presence of promoter -binding elements, microRNAs, and long non-coding RNAs (lncRNA). This approach revealed new uncharacterized transcription factors together with several microRNAs potentially regulating different steps of the phenylpropanoid pathway, and one particular lncRNA compromising the expression of nine stilbene synthase genes located in chromosome 10. Application of network-based approaches into multi-omics data will continue providing supplementary resources to address important questions regarding grapevine fruit quality and composition.
将大型生物数据表示为网络，在预测基因功能的同时阐明生命过程的多方面组织，正越来越多地被采用。在葡萄（Vitis vinifera L.）中，网络分析大多被用于帮助理解控制浆果成分的调控机制。然而，一些研究使用基因共表达网络来寻找与发育和代谢相关的转录因子的共同途径和推定靶点，另一些研究则定义了初级和次级代谢物网络，以表征整个果实成熟过程中不同品种之间的主要代谢差异。最近，还生成了与蛋白质组学相关的网络以及整合启动子调控元件全基因组分析的网络。将所有这些数据整合到多层网络中，可以构建分子调控和相互作用的复杂图谱。这篇观点文章描述了目前可用的葡萄网络数据及相关资源。为了说明将数据整合到葡萄网络构建和分析中的方法，我们搜索了苯丙烷类途径的浆果特异性调控因子。我们生成了一个复合网络，该网络由结构基因和转录因子基因之间的共表达图谱叠加而成，并整合了启动子结合元件、微小RNA和长链非编码RNA（lncRNA）的存在情况。这种方法揭示了新的未表征转录因子以及几种可能调控苯丙烷类途径不同步骤的微小RNA，还有一个特定的lncRNA影响位于10号染色体上的9个芪合酶基因的表达。将基于网络的方法应用于多组学数据将继续提供补充资源，以解决有关葡萄果实品质和成分的重要问题。
