bioRxivSystems BiologyPreprint
Integrative system biology analysis of barley transcriptome - hormonal signaling against biotic stress
Zahra SoltaniiD, Ali Asghar MoghadamiD, Ahmad TahmasebiiD, Ali NiaziiD
Abstract
Biotic stresses are environmental factors that cause a variety of crop diseases and damages. In contrast, crops trigger specific transduction signaling pathways that the hormones are the central players. Integrative OMICS for systems genetic engineering approach contributes in the understanding of molecular mechanisms. In this research, the system biology approaches were applied to discover particular molecular interactions between biotic stresses and hormonal signaling in barley. The meta-analysis of the data identified a total of 1232 and 304 differentially expressed genes (DEGs) respectively so that were significantly involved in defense processes and hormone signaling. A total of 24 TFs belonged to 15 conserved families and 6 TFs belonged to 6 conserved families were identified for biotic and hormonal data respectively, whereas NF-YC, GNAT, and whirly families were the most abundant groups. The functional analysis of the upstream regions for over-represented cis-acting elements revealed that were involved activation of transcription factors in response to pathogens and hormones. Based on the co-expression analysis, 6 and 7 distinct co-expression modules related to biotic stresses and hormonal signaling were respectively uncovered. The gene network analysis also identified novel hub genes such as TIM10, DRT101, ADG1, and TRA2 which may be involved in regulating defense responses to biotic stresses. In addition, many new genes with unknown function were obtained. Since this study represents a first preliminary curated system biology analysis of barley transcriptomic responses to biotic stresses and hormone treatments, introduces important candidate genes that may be beneficial to crop biotechnologists to accelerate genetic engineering programs.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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