Abstract
As a globally important food crop, Maize is significantly affected in yield and growth by drought stress. Due to the complex multigene regulatory networks governing maize drought resistance traits, single-omics data alone are insufficient to reveal its regulatory mechanisms comprehensively. This study integrates multi-omics data, including genomics, transcriptomics, epigenomics, and metabolomics, to systematically construct a maize drought resistance gene regulatory network. Through big data analysis, combined with multidimensional data under various environmental and stress conditions, key gene nodes and core transcription factors within the drought-resistance regulatory network were identified. Utilizing artificial intelligence (AI) algorithms, such as random forest and deep learning, we extensively explored gene interactions and identified gene combinations contributing significantly to drought resistance traits. Subsequently, CRISPR/Cas9 gene editing technology was employed to validate the functions of these key genes, preliminarily exploring their application potential in drought-resistance breeding. The results indicate that integrating multi-omics data with AI algorithms significantly enhances the capacity to analyze complex gene regulatory networks and improves the efficiency of drought-resistance gene selection. This study offers new insights into the molecular regulatory mechanisms of maize drought resistance traits and provides strong candidate genes and strategic support for precision breeding.
