Comparative analysis of waterlogging and drought stress regulatory networks in barley (Hordeum vulgare).

We applied a systems biology approach to gain a deep insight into the regulatory mechanisms of barley (Hordeum vulgare ) under drought and waterlogging stress conditions. To identify informative models related to stress conditions, we constructed meta-analysis and two distinct weighted gene co-expression networks. We then performed module trait association analyses. Additionally, we conducted functional enrichment analysis of significant modules to shed light on the biological performance of underlying genes in the two contrasting stresses. In the next step, we inferred the gene regulatory networks between top hub genes of significant modules, kinases, and transcription factors (TFs) using a machine learning algorithm. Our results showed that at power=10, the scale-free topology fitting index (R2) was higher than 0.8 and the connectivity mean became stable. We identified 31 co-expressed gene modules in barley, with 13 and 14 modules demonstrating significant associations with drought and waterlogging stress, respectively. Functional enrichment analysis indicated that these stress-responsive modules are involved in critical processes, including ADP-rybosylation factors (ARF) protein signal transduction, ethylene-induced autophagy, and phosphoric ester hydrolase activity. Specific TFs and kinases, such as C2C2-GATA, HB-BELL, and MADS-MIKC, were identified as key regulators under these stress conditions. Furthermore, certain TFs and kinases established unique connections with hub genes in response to waterlogging and drought conditions. These findings enhance our understanding of the molecular networks that modulate barley's response to drought and waterlogging stresses, offering insights into the regulatory mechanisms essential for stress adaptation.