Comprehensive Understanding of the Interaction Among Stress Hormones Signalling Pathways by Gene Co-expression Network

Functional characterization of CiHY5 in salt tolerance of Chrysanthemum indicum and conserved role of HY5 under stress in chrysanthemum

Among various abiotic stresses, secondary soil salinization poses a significant threat to plant productivity and survival. Cultivated chrysanthemums (Chrysanthemum morifolium), widely grown as ornamental crops, are highly susceptible to salt stress, and their complex polyploid genome complicates the identification of stress resistance genes. In contrast, C. indicum, a native diploid species with robust stress tolerance, serves as a valuable genetic resource for uncovering stress-responsive genes and improving the resilience of ornamental chrysanthemum cultivars. In this study, we cloned, overexpressed (OE-CiHY5), and silenced (RNAi-CiHY5) the CiHY5 gene in C. indicum. OE-CiHY5 plants exhibited larger leaves, sturdier stalks, and higher chlorophyll content compared to wild-type plants, while RNAi-CiHY5 plants displayed weaker growth. Under salt stress, OE-CiHY5 plants demonstrated significantly improved growth, enhanced osmotic adjustment, and effective ROS scavenging. In contrast, RNAi-CiHY5 plants were more sensitive to salinity, showing higher electrolyte leakage and impaired osmotic regulation. Transcriptomic analyses revealed that CiHY5 regulates key hormonal pathways such as zeatin (one of cytokinins), abscisic acid and jasmonic acid, as well as metabolic pathways, including photosynthesis, carbohydrate metabolism, which collectively contribute to the enhanced stress resilience of OE-CiHY5 plants. Promoter-binding assays further confirmed that CiHY5 directly interacts with the CiABF3 promoter, highlighting its critical role in ABA signaling. Evolutionary analyses showed that HY5 is conserved across plant lineages, from early algae to advanced angiosperms, with consistent responsiveness to salt and other abiotic stresses in multiple Chrysanthemum species. These findings establish CiHY5 as a key regulator of salt tolerance in C. indicum, orchestrating a complex network of hormonal and metabolic pathways to mitigate salinity-induced damage. Given the conserved nature of HY5 and its responsiveness to various stresses, HY5 gene provides valuable insights into the molecular mechanisms underlying stress adaptation and represents a promising genetic target for enhancing salt stress resilience in chrysanthemums.

Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body's life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.

Application of Multilayer Network Models in Bioinformatics

Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.