Liang Ying-Hai, Cai Bin, Chen Fei, Wang Gang, Wang Min, Zhong Yan, Cheng Zong-Ming Max
College of Horticulture, Nanjing Agricultural University , Nanjing 210095, China ; Institute of Pomology, Academy of Agricultural Sciences of Jilin Province , Gong Zhuling 136100, China.
College of Horticulture, Nanjing Agricultural University , Nanjing 210095, China.
Hortic Res. 2014 Aug 13;1:14040. doi: 10.1038/hortres.2014.40. eCollection 2014.
Gene co-expression analysis has been widely used for predicting gene functions because genes within modules of a co-expression network may be involved in similar biological processes and exhibit similar biological functions. To detect gene relationships in the grapevine genome, we constructed a grapevine gene co-expression network (GGCN) by compiling a total of 374 publically available grapevine microarray datasets. The GGCN consisted of 557 modules containing a total of 3834 nodes with 13 479 edges. The functions of the subnetwork modules were inferred by Gene ontology (GO) enrichment analysis. In 127 of the 557 modules containing two or more GO terms, 38 modules exhibited the most significantly enriched GO terms, including 'protein catabolism process', 'photosynthesis', 'cell biosynthesis process', 'biosynthesis of plant cell wall', 'stress response' and other important biological processes. The 'response to heat' GO term was highly represented in module 17, which is composed of many heat shock proteins. To further determine the potential functions of genes in module 17, we performed a Pearson correlation coefficient test, analyzed orthologous relationships with Arabidopsis genes and established gene expression correlations with real-time quantitative reverse transcriptase PCR (qRT-PCR). Our results indicated that many genes in module 17 were upregulated during the heat shock and recovery processes and downregulated in response to low temperature. Furthermore, two putative genes, Vit_07s0185g00040 and Vit_02s0025g04060, were highly expressed in response to heat shock and recovery. This study provides insight into GGCN gene modules and offers important references for gene functions and the discovery of new genes at the module level.
基因共表达分析已被广泛用于预测基因功能,因为共表达网络模块内的基因可能参与相似的生物学过程并表现出相似的生物学功能。为了检测葡萄基因组中的基因关系,我们通过汇编总共374个公开可用的葡萄微阵列数据集构建了一个葡萄基因共表达网络(GGCN)。GGCN由557个模块组成,总共包含3834个节点和13479条边。通过基因本体论(GO)富集分析推断子网模块的功能。在557个包含两个或更多GO术语的模块中,有127个模块,其中38个模块表现出最显著富集的GO术语,包括“蛋白质分解代谢过程”、“光合作用”、“细胞生物合成过程”、“植物细胞壁生物合成”、“应激反应”和其他重要的生物学过程。“热响应”GO术语在模块17中高度富集,该模块由许多热休克蛋白组成。为了进一步确定模块17中基因的潜在功能,我们进行了Pearson相关系数测试,分析了与拟南芥基因的直系同源关系,并通过实时定量逆转录聚合酶链反应(qRT-PCR)建立了基因表达相关性。我们的结果表明,模块17中的许多基因在热休克和恢复过程中上调,而在低温响应中下调。此外,两个假定基因Vit_07s0185g00040和Vit_02s0025g04060在热休克和恢复过程中高度表达。本研究深入了解了GGCN基因模块,并为基因功能以及在模块水平发现新基因提供了重要参考。