Mining Heat-Resistant Key Genes of Peony Based on Weighted Gene Co-Expression Network Analysis.
Xingyu YangYu HuangYiping YaoWenxuan BuMinhuan ZhangTangchun ZhengXiaoning LuoZheng WangWeiqun LeiJianing TianLujie ChenLiping QinPublished in: Genes (2024)
The RNA-Seq and gene expression data of mature leaves under high temperature stress of Paeonia suffruticosa 'Hu Hong' were used to explore the key genes of heat tolerance of peony. The weighted gene co-expression network analysis (WGCNA) method was used to construct the network, and the main modules and core genes of co-expression were screened according to the results of gene expression and module function enrichment analysis. According to the correlation of gene expression, the network was divided into 19 modules. By analyzing the expression patterns of each module gene, Blue, Salmon and Yellow were identified as the key modules of peony heat response related functions. GO and KEGG functional enrichment analysis was performed on the genes in the three modules and a network diagram was constructed. Based on this, two key genes PsWRKY53 (TRINITY_DN60998_c1_g2, TRINITY_DN71537_c0_g1) and PsHsfB2b (TRINITY_DN56794_c0_g1) were excavated, which may play a key role in the heat shock response of peony. The three co-expression modules and two key genes were helpful to further elucidate the heat resistance mechanism of P. suffruticosa 'Hu Hong'.
Keyphrases
- network analysis
- genome wide
- gene expression
- genome wide identification
- poor prognosis
- dna methylation
- heat stress
- rna seq
- genome wide analysis
- bioinformatics analysis
- heat shock
- transcription factor
- binding protein
- magnetic resonance
- magnetic resonance imaging
- high temperature
- heat shock protein
- deep learning
- big data