Maize Genomics and Genetics 2025, Vol.16, No.4, 202-218 http://cropscipublisher.com/index.php/mgg 216 tolerance but reduces photosynthetic yield). How to balance the responses to multiple stresses is also a problem that needs to be considered. Technically, accurately simulating field heat stress conditions for research remains challenging. High temperatures in the field are often accompanied by changes in the day-night cycle, light conditions and moisture levels. The constant temperature treatment under laboratory control conditions differs from the actual environment. This may lead to some experimental results being difficult to reproduce in the field. Therefore, future research needs to develop stress test systems that are closer to real environments, such as intelligent field greenhouses and mobile greenhouses, to enhance the application relevance of research results. Finally, the corn genome is large and polyploid, with redundant functional genes and a complex genetic background, which increases the difficulty of gene mining. The application of gene editing in corn also faces problems such as low transformation efficiency and needs further improvement. In conclusion, the research on the heat tolerance of corn is still in the stage of deepening from macroscopic phenomena to microscopic mechanisms. We not only need to discover "which genes" are involved, but also clarify "how these genes work together". Solving these problems will help to comprehensively reveal the heat tolerance mechanism of corn and transform it into stable and high-yield genetic improvement results. Facing the complex trait of heat tolerance in corn, the future research trend will be the integration of multi-level omics data and systems biology analysis. Single transcriptome studies have revealed a wealth of information, but if genomic, proteomic, metabolomic and other data can be combined, it is expected to more comprehensively depict the response network of corn to high-temperature stress. For instance, through genomics and association analysis, key genetic variation sites that affect heat tolerance can be located, while proteomics can identify functional proteins that undergo modification or abundance changes at high temperatures, and metabolomics reflects the alterations in the physiological metabolic state of corn under heat stress. Combining these omics data with transcriptomics can establish a system regulatory model of gene-protein-metabolites. For instance, for a key gene that is upregulated at high temperatures, proteomic data can be used to confirm whether the encoded protein has accumulated or undergone post-translational modifications, and metabolomic data can be used to verify whether its downstream metabolites have changed accordingly, thereby verifying the heat tolerance function of the gene from multiple aspects. In recent years, some studies have begun to explore this direction. The research group led by Li Lin from Huazhong Agricultural University and others have constructed the first multi-omics integration network for corn, uniformly analyzing three-dimensional genomic, transcriptomic, proteomic and other information. They found that approximately 30 000 corn genes jointly participate in stress response through different levels of association. It can be foreseen that the introduction of omics integration in heat resistance research will help discover regulatory relationships that cannot be detected by a single omics. For instance, some genes that do not show differential expression in the transcriptome may play a role in heat stress through improved translation efficiency or enhanced protein stability, which requires proteomic data to capture. Or, changes in the metabolome can suggest adjustments in biochemical pathways not covered by the transcriptome. For instance, the content of certain osmotic protective substances increases under heat stress, and the key enzyme genes responsible for their synthesis may not be detected as DEG. However, by discovering this phenomenon through the metabolome and then delve deeper into the transcriptome, regulatory clues can be found. In the future, new technologies such as single-cell sequencing and spatial transcriptomics can be utilized to analyze the expression changes of different cell types in corn under heat stress, and combined with traditional omics to more precisely construct a heat tolerance regulatory network. At the application level, multi-omics integration can also assist in breeding decisions. For instance, combined genome-transcriptome analysis can be used for the association between genotypes and expression types, and to screen out genes that have both excellent alleles and are highly expressed under heat conditions for breeding markers. Proteomic-metabolome binding can be used to identify reliable heat tolerance biomarkers for rapid field evaluation of variety heat tolerance. In conclusion, with the development of sequencing and analysis technologies, cross-omics integration will enable us to understand the complex system network of corn heat tolerance at an overall level, and thereby guide more rational and efficient genetic improvement strategies. Through the integration of different disciplines, the biological research on corn stress resistance is moving from a "one-dimensional" to a "multi-dimensional" approach. In the future, it is expected to achieve precise design and modification of corn heat tolerance, contributing to ensuring global food production and responding to climate change.
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