Maize Genomics and Genetics 2025, Vol.16, No.4, 202-218 http://cropscipublisher.com/index.php/mgg 209 ROS signaling: for instance, in Arabidopsis thaliana, MPK6 phosphorylates the WRKY and ZAT transcription factors, the latter of which regulates the expression of antioxidant enzyme genes; A similar mechanism in corn remains to be further verified, but it is speculated that MAPK-ROS forms a positive feedback loop at high temperatures, helping to enhance stress resistance signals. Overall, the Ca2+ signal, ABA hormone and MAPK cascade constitute the core thoroughfares of the maize heat stress signal network. They are interlinked: Ca2+ and ABA signals can amplify each other through the MAPK pathway, and the effect of MAPK depends on the initial Ca2+ signal and ABA level. This multi-pathway coupled regulation ensures that corn can rapidly and harmoniously activate defense genes and adaptive physiological responses under heat stress, achieving efficient signal transmission from perception to response. 4.3 Gene co-expression network analysis revealing key regulatory modules By leveraging high-throughput transcriptome data, researchers applied gene co-expression network analysis (methods such as WGCNA) to identify key gene modules and regulatory centers in the heat stress response of maize. By classifying genes with similar expression patterns under different high-temperature treatment conditions into one category of modules, some co-expressed gene groups closely related to heat-resistant phenotypes can be discovered. For instance, Cao et al. (2021) conducted a weighted co-expression network analysis of the transcriptomes of five maize inbred lines treated at 45 °C, dividing 17 062 differentially expressed genes into 19 modules. Among them, the "turquoise green" module contained 6 089 genes and was significantly upregulated under high-temperature stress, and was regarded as a key module for maize's heat tolerance response. The genes of this module are enriched in the pathways related to heat response and reactive oxygen species clearance, and contain multiple genes encoding heat shock proteins and antioxidant enzymes, suggesting that its function is directly related to heat tolerance. Network analysis further reveals that in the co-expression relationship of these genes, several transcription factor genes are in a "central" position. For instance, module enrichment analysis revealed that the transcription factor genes of the HSF, ERF and bZIP families were highly enriched in the corn high-temperature response module, belonging to the "hub" nodes in the network. This is consistent with the findings of Li et al. (2024): They conducted a heat stress time series transcriptome analysis on the maize inbred line B73 and identified a gene module highly correlated with heat response using WGCNA. In this module, HSF-like transcription factors were significantly enriched and had the highest connectivity. In this co-expression subnetwork with HSF as the hub, not only numerous HSP and defense genes are included, but also some signaling pathway elements and secondary metabolic genes are incorporated, reflecting the systemic response triggered by heat stress. In addition to the core regulatory module, the co-expression network can also help identify new candidate heat-resistant genes. Tang et al. (2023) compared the transcriptomes of heat-tolerant and sensitive maize inbred lines and conducted an association analysis of 142 heat-responsive core genes shared by both with known heat-tolerant QTLS. They found that 42 genes were located in the reported heat-tolerant QTL intervals, including multiple genes encoding transcriptional regulatory factors and molecular partners. These genes are very likely the key candidate genes that affect the heat tolerance of corn. In addition, network analysis can also reveal the regulatory relationships among genes. For instance, Li et al. (2024) proposed through network inference that ZmHSF20-ZmHSF4-ZmcesA2 constitutes a regulatory module: ZmHSF20 inhibits the expression of ZmHSF4 and ZmCesA2, while ZmHSF4 directly activates ZmCesA2, thereby jointly regulating the balance between cell wall synthesis and thermal defense. This example demonstrates that network analysis can concatenate scattered genes into meaningful regulatory units. The gene co-expression network provides an effective means for analyzing the complex regulatory relationship of high-temperature response in corn. By identifying the high-temperature upregulation module and the hub genes within it, researchers can identify the key regulatory modules and genes that contribute significantly to heat tolerance. On this basis, they can further carry out functional verification and breeding applications. 5 Expression Specificity and Dynamic Regulatory Features 5.1 Tissue-specific responses of roots, leaves, and stems under heat stress There are significant differences in the sensitivity and response patterns of different tissues and organs of corn to high-temperature stress. During the seedling stage, the root system, leaves and stems respectively undertake
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