Distributed Information Centre, Assam Agricultural University, Jorhat 785013, Assam, India; DBT-North East Centre for Agricultural Biotechnology (DBT-NECAB), Assam Agricultural University, Jorhat 785013, Assam, India.
Distributed Information Centre, Assam Agricultural University, Jorhat 785013, Assam, India.
Gene. 2019 May 25;698:82-91. doi: 10.1016/j.gene.2019.02.063. Epub 2019 Feb 27.
Differential co-expression is a cutting-edge approach to analyze gene expression data and identify both shared and divergent expression patterns. The availability of high-throughput gene expression datasets and efficient computational approaches have unfolded the opportunity to a systems level understanding of functional genomics of different stresses with respect to plants. We performed the meta-analysis of the available microarray data for reoviridae and sequiviridae infection in rice with the aim to identify the shared gene co-expression profile. The microarray data were downloaded from ArrayExpress and analyzed through a modified Weighted Gene Co-expression Network Analysis (WGCNA) protocol. WGCNA clustered the genes based on the expression intensities across the samples followed by identification of modules, eigengenes, principal components, topology overlap, module membership and module preservation. The module preservation analysis identified 4 modules; salmon (638 genes), midnightblue (584 genes), lightcyan (686 genes) and red (562 genes), which are highly preserved in both the cases. The networks in case of reoviridae infection showed neatly packed clusters whereas, in sequiviridae, the clusters were loosely connected which is due to the differences in the correlation values. We also identified 83 common transcription factors targeting the hub genes from all the identified modules. This study provides a coherent view of the comparative aspect of the expression of common genes involved in different virus infections which may aid in the identification of novel targets and development of new intervention strategy against the virus.
差异共表达是一种分析基因表达数据的前沿方法，可以识别共享和发散的表达模式。高通量基因表达数据集的可用性和高效的计算方法为我们提供了一个机会，可以从系统层面理解不同应激条件下植物的功能基因组学。我们对水稻呼肠孤病毒和正黏病毒感染的现有微阵列数据进行了荟萃分析，目的是识别共享的基因共表达谱。微阵列数据从 ArrayExpress 下载，并通过修改后的加权基因共表达网络分析（WGCNA）方案进行分析。WGCNA 根据样本间的表达强度对基因进行聚类，然后识别模块、特征基因、主成分、拓扑重叠、模块成员和模块保存。模块保存分析确定了 4 个模块；salmon（638 个基因）、midnightblue（584 个基因）、lightcyan（686 个基因）和 red（562 个基因），这 4 个模块在两种情况下都高度保存。呼肠孤病毒感染的网络显示出整齐的聚类，而正黏病毒的聚类则连接松散，这是由于相关值的差异造成的。我们还从所有鉴定的模块中鉴定了 83 个针对枢纽基因的共同转录因子。这项研究提供了一个关于不同病毒感染中共同基因表达的比较方面的连贯视图，这可能有助于识别新的靶标和开发针对病毒的新干预策略。
