利用WGCNA鉴定甘蓝型油菜低温胁迫的基因共表达网络

Identification of co-expression networks responding to low-temperature stress by WGCNA in Brassica napus L.

为了解甘蓝型油菜受低温胁迫时的共表达网络,运用加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)方法,利用2个抗冻响应有差别的甘蓝型油菜材料HX17和HX58不同低温处理的36个RNA-seq数据,通过过滤低表达基因,筛选表达水平变异最大的前15 000个基因用于WGCNA分析,共鉴定到16个基因共表达模块。结合转录因子预测、冷相关基因预测、模块基因的gene ontology(GO)富集及表达分析,确定了brown模块为响应冻害胁迫的共同模块、pink模块为HX17耐寒特异性模块,并构建了基因共表达网络,为油菜抗寒/抗冻机制研究提供新的依据。

To better understand the gene expressions under low lemperature, weighted gene co-expression network analysis(WGCNA) was used on rapeseed(Brassica napus L.) co-expression patterns under cold stress. 36 RNA-seq data generated from HX17 and HX58(2 materials had different freezing responses) with different lowtemperature stresses were used as data resource. After filtering out the low-expression genes, the top 15 000 genes with the largest variation in expression level were screened for WGCNA. A total of 16 co-expression modules were identified. Combined with transcription factor prediction, cold-related gene prediction, gene ontology(GO) enrichment, and gene expression analysis, brown module was determined as the common module in response to freezing stress, pink module as the specific module of cold tolerance of HX17. Furthermore, the gene co-expression networks were constructed, which could provide new insights and support for research in cold/frost tolerance mechanism of rapeseed.