Integrating Hormonal, Kinase and Transcription Factor Regulatory Networks in Plant Salinity Stress: Toward a Hierarchical Signal Integration Model

Soil salinity imposes a complex combination of ionic, osmotic, and oxidative stresses that disrupt plant cellular homeostasis and significantly limit crop productivity. Over the past decades, major signaling components of salinity responses, including Ca2+ and reactive oxygen species (ROS) signaling, abscisic acid (ABA) pathways, kinase networks and transcription factor (TF)-mediated gene regulation have been extensively characterized. However, existing models remain largely pathway-centric and do not adequately explain how these components are hierarchically organized and dynamically integrated into coherent adaptive responses. Here, we synthesize current knowledge across multiple regulatory layers, spanning early stress perception and Ca2+-ROS amplification, kinase-mediated signal integration, transcriptional regulation, post-translational control and emerging mechanisms such as miRNAs and epigenetic modifications. We highlight that while core modules, including CBL-CIPK-SOS signaling, PYR/PYL-PP2C-SnRK2 ABA pathways and MAPK/CDPK-mediated phosphorylation are strongly supported by biochemical and genetic evidence, many higher-order interactions remain partially validated or inferred. To address these limitations, we propose a Hierarchical Signal Integration Model (HSIM) that organizes salinity signaling into three functional layers: input (ionic and osmotic sensing), integration (ABA signaling, kinase networks, and Ca2+-ROS feedback modules) and output (transcriptional and post-translational regulation). Within this framework, kinase hubs emerge as central nodes of signal convergence and amplification, whereas ABA functions as a context-dependent regulator rather than a universally dominant pathway. By emphasizing hierarchical organization, feedback dynamics, and context-dependent interactions, HSIM provides a testable framework for understanding signal prioritization, robustness and growth-stress trade-offs, and offers a foundation for predictive modeling and rational engineering of salinity tolerance in crops.