Network Inference Reveals Distinct Transcriptional Regulation in Barley against Drought and Fusarium Head Blight

Abstract

We analyzed transcriptional networks in barley under single and combined Fusarium head blight (FHB) and drought stress. We applied complementary Weighted Gene Correlation Network Analysis (WGCNA) to identify stress-associated gene co-expression modules and GENIE3 to infer gene regulatory networks (GRNs). Integration of these frameworks revealed strong overlaps between co-expression modules and GRN clusters, highlighting robust regulatory patterns. Key transcription factors (TFs) were identified based on their weighted node degrees, reflecting their connectivity within the network. Independent analysis of paired transcription factor binding sites in promoter regions further supported predicted regulatory interactions. Notably, WRKY TFs emerged as central regulators of FHB response, consistent with their known roles in defense and secondary metabolite biosynthesis, but did not appear in drought-associated contexts. For bHLH or NAC TFs, individual family members steered FHB or drought responses but not both. Our findings demonstrate the power of combining network inference and motif enrichment to identify candidate TFs controlling stress responses, providing a solid foundation for targeted functional validation.