1 Introduction

Maize (Zea mays) is a cornerstone of global agriculture, serving as a staple food for millions and a critical raw material for diverse industrial applications. Its extensive cultivation and utilization underpin food security, livestock feed, and bioenergy production, making advances in maize biology essential for addressing global challenges in nutrition, sustainability, and economic development (Walley et al., 2016; Zhou et al., 2020).

Gene regulatory networks (GRNs) orchestrate the complex interplay between transcription factors and their target genes, governing key biological processes such as growth, development, and responses to environmental stimuli in maize (Walley et al., 2016; Yang et al., 2017; Huang et al., 2018; Zhou et al., 2020; Yuan et al., 2024). These networks enable precise spatial and temporal control of gene expression, facilitating tissue-specific functions, developmental transitions, and adaptation to biotic and abiotic stresses (Yang et al., 2017; Huang et al., 2018; Abnave et al., 2024; Wang et al., 2024; Yuan et al., 2024). Recent functional genomics approaches, including large-scale transcriptomics, proteomics, and single-cell analyses, have illuminated the architecture and dynamics of maize GRNs, revealing their centrality in metabolic regulation, organ differentiation, and stress resilience (Walley et al., 2016; Yang et al., 2017; Huang et al., 2018; Chang et al., 2019; Zhou et al., 2020; Abnave et al., 2024; Yuan et al., 2024).

This study will explore the research progress of functional genomics in the structure and function of maize gene regulatory networks in recent years, and integrate the achievements of transcriptome association studies, network modeling and multi-omics methods. This study aims to highlight the progress in understanding how GRNs regulate maize development, metabolism and environmental adaptation, identify emerging research hotspots, and discuss the impact of GRN research on crop improvement and sustainable agricultural development.

2 Core characteristics of the maize gene regulatory system

2.1 Core components of the regulatory system

The maize gene regulatory system is mainly composed of regulatory proteins, DNA regulatory regions and functional Rnas. Regulatory proteins achieve the activation or inhibition of target genes by binding to specific regions of DNA (such as promoters, enhancers, etc.) (Yang et al., 2017; Huang et al., 2018; Tu et al., 2020). These core regulatory factors form functional modules through combined action and dominate the specific gene expression patterns during the differentiation and development stages of maize tissues (Yang et al., 2017; Huang et al., 2018; Tu et al., 2020).

Functional RNAs (such as micrornas) play an important role in the maize gene network by regulating the gene translation process. Studies have shown that miRNA399b plays a key role in the nitrogen response pathway, indicating that functional RNA can precisely regulate the expression of genes related to environmental responses (Jiang et al., 2018). These components jointly constitute a multi-level dynamic regulation system, supporting the growth, development and environmental adaptation processes of corn (Yang et al., 2017; Jiang et al., 2018).

2.2 Network characteristics and hierarchical architecture of the regulation system

The maize gene regulatory system is highly complex, manifested as dense connection of network nodes, independent operation of functional modules and multi-level architecture. Genomic studies have shown that this network presents a feature dominated by hub nodes: a few core regulatory proteins are connected to a large number of target genes, while most nodes are only involved in local regulation (Tu et al., 2020; Abnave et al., 2024). This architecture enhances the stability and plasticity of the system, enabling corn to respond rapidly to developmental signals and environmental changes (Tu et al., 2020; Abnave et al., 2024).

The hierarchical characteristics of the network are significant, manifested as the upper-level regulatory factors dominating the global metabolic program, and the lower-level regulatory units being responsible for the fine-tuning of local gene sets (Abnave et al., 2024). For example, in the phenolic synthesis pathway, the upper regulatory proteins coordinate the expression of multiple genes, while the cross-level regulatory mechanism achieves precise regulation of metabolites by integrating developmental and stress signals (Abnave et al., 2024). This architecture significantly enhances the adaptability of corn to complex environments.

2.3 The influence of the regulatory system on important agronomic traits

The gene regulatory network dominates key traits such as organ development, metabolic regulation and stress resistance in maize. The research has constructed organ-specific regulatory networks such as leaves, roots, and stem tip meristematic tissues, revealing that different tissues achieve functional differentiation through specific regulatory modules (Huang et al., 2018; Wang et al., 2024). For example, the phenolic metabolism regulatory network regulates the disease resistance and mechanical strength of plants by coordinating the expression of genes related to lignin and flavonoid synthesis (Yang et al., 2017; Abnave et al., 2024).

Furthermore, the regulatory network plays a decisive role in how corn responds to environmental stresses (such as nutrient deficiency, drought, etc.). The network composed of regulatory proteins, DNA regulatory regions and functional Rnas directly affects yield and quality by regulating processes such as nutrient absorption and the expression of stress-resistant genes (Jiang et al., 2018; Wang et al., 2024). Analyzing these regulatory mechanisms provides an important theoretical basis for molecular breeding.

3 Analysis of Maize Regulatory Networks Using Functional Genomics Techniques

3.1 Application of gene expression profile analysis technology

Gene expression profiling analysis techniques (such as high-throughput RNA sequencing) provide core support for understanding the dynamic expression patterns of maize genes and constructing regulatory networks. Through systematic detection of samples from multiple tissues and multiple developmental stages, researchers have established a regulatory network based on the co-expression model and successfully identified the targets of regulatory proteins and related regulatory factors under different conditions (Huang et al., 2018; Zhou et al., 2020). These network maps clearly present the interaction relationship between regulatory proteins and target genes, laying the foundation for analyzing the regulatory mechanism of maize growth and development (Huang et al., 2018; Zhou et al., 2020).

Combining gene expression data with omics information such as protein interactions and phosphorylation modifications significantly improves the accuracy of network predictions. For instance, the multi-omics map of maize development shows that although the co-expression networks constructed based on the transcriptome and proteome have hub differences, they are all enriched in the same metabolic pathways (Walley et al., 2016). This cross-omics joint analysis enhances the analytical accuracy of functional gene interaction relationships and regulatory modules.

3.2 Progress in epigenetic modification detection technology

DNA methylation detection and histone modification analysis techniques provide a new perspective for revealing the epigenetic regulatory mechanism of maize. The application of methods such as sulfite sequencing and chromatin open region detection enables researchers to precisely locate DNA methylation sites and active regulatory regions, and clarify how epigenetic modification characteristics affect gene expression (Rodgers-Melnick et al., 2016; Springer et al., 2018). Studies have shown that although there are only a few open chromatin regions in the maize genome, these regions play an important regulatory role in phenotypic variations (Rodgers-Melnick et al., 2016; Zhou and Jiang, 2024).

The three-dimensional structural characteristics of chromatin are closely related to gene regulation. Through chromatin accessibility experiments, scientists have mapped the open chromatin of active genes and recombination hotspots in corn and found that these regions significantly affect the transmission of genetic traits (Rodgers-Melnick et al., 2016). By combining epigenetic data with gene expression information, researchers can gain a deeper understanding of the interaction mechanism between chromatin state and gene regulation.

3.3 Breakthroughs in Single-cell Technology and three-dimensional genome technology

The application of single-cell sequencing technology has achieved a fine analysis of the specific regulatory network of corn cells. By detecting the gene expression characteristics of individual cells, researchers can identify the key regulatory modules that drive tissue differentiation and functional specialization (Huang et al., 2018). This high-precision technology can construct regulatory networks at the cellular level and clarify the specific mechanisms of action of regulatory proteins in different cell types.

Three-dimensional genomic techniques (such as Hi-C) have revealed the influence of chromatin spatial conformation on gene regulation. By capturing the spatial interaction relationship between regulatory elements and target genes, this technique provides direct evidence for analyzing the three-dimensional structure of the regulatory network. The combination of single-cell and spatial omics technologies is promoting in-depth research on the mechanisms of maize development regulation and environmental response.

4 Case Studies of Gene Regulatory Networks Related to Important Maize Traits

4.1 GRNs regulating developmental processes

Gene regulatory networks (GRNs) play a central role in orchestrating key developmental transitions in maize, such as the shift from vegetative to reproductive growth. For flowering time control, a conceptual GRN model has been developed based on the interactions among more than 30 candidate genes identified through comparative genomics, mutant analysis, and QTL mapping (Dong et al., 2012). This model incorporates dynamic gene expression data from various genotypes, including late-flowering mutants and early-flowering landraces, to predict phenotypic outcomes and guide breeding strategies for optimal flowering time (Dong et al., 2012; Cai, 2024).

Beyond flowering, GRNs also regulate other developmental processes such as tiller development. For example, the Tin8 locus, encoding a PEBP-related kinase, interacts with the well-known Tb1 gene to control tiller number by modulating the expression of multiple downstream genes, including those involved in hormonal responses (Figure 1) (Lin et al., 2021). These findings highlight the complexity and interconnectedness of developmental GRNs, which integrate genetic and environmental signals to fine-tune plant architecture.

4.2 Gene regulatory networks involved in stress resistance

GRNs are crucial for maize’s ability to respond to environmental stresses by coordinating the expression of stress-responsive genes. The OPAQUE11 (O11) transcription factor serves as a central hub in the endosperm regulatory network, directly regulating genes involved in nutrient metabolism and stress responses (Feng et al., 2018). O11 interacts with other transcription factors and proteins, such as ZmIce1, to co-regulate stress response pathways and developmental processes, demonstrating the integration of metabolic and stress adaptation networks (Feng et al., 2018).

Additionally, coexpression-based GRNs constructed from thousands of transcriptome datasets have identified transcription factors and candidate regulators associated with important metabolic pathways and stress responses (Zhou et al., 2020). These networks reveal that presence or absence of specific TFs, rather than their quantitative expression levels, often drives changes in target gene expression, providing potential targets for breeding stress-resistant maize varieties (Zhou et al., 2020).

4.3 Networks controlling yield and quality traits

Yield and quality traits in maize are governed by complex GRNs involving multiple gene families and regulatory modules. Analyses of yield-related traits, such as seed size, weight, and shape, have identified key gene families-APETALA2/ethylene response factors, auxin response factors, and MADS-box genes-as central components of yield-related GRNs (Chen et al., 2020). These networks provide theoretical and practical guidance for improving crop yield through molecular breeding (Chen et al., 2020).

Modern maize breeding has leveraged GRN insights to enhance yield and plant architecture. By integrating gene expression, eQTL, and coexpression network analyses, researchers have identified candidate genes and regulatory loci, such as ZmARF16, ZmTCP40, and ZmPIF3.3, that contribute to improved yield, plant height, and kernel row number (Li et al., 2023). These findings underscore the value of GRN analysis in uncovering the genetic basis of agronomic traits and guiding the development of high-yield, high-quality maize cultivars.

5 Data Integration and Analytical Methods in Functional Genomics of Maize GRNs

5.1 Multi-omics data integration

Integrating multiple omics layers-such as transcriptomics, proteomics, and phosphoproteomics—has greatly enhanced the predictive power and biological relevance of maize gene regulatory networks (GRNs). For example, a developmental atlas of maize combined mRNA, protein, and phosphoprotein data from the same tissue samples, revealing that coexpression networks based on different data types often highlight distinct regulatory hubs, yet both are enriched for similar biological pathways (Walley et al., 2016). This integrative approach allows for a more comprehensive understanding of gene regulation, capturing both transcriptional and post-transcriptional control mechanisms.

Epigenomic data, including DNA methylation and chromatin accessibility profiles, are also integrated with transcriptomic and cis-regulatory element (CRE) mapping to refine GRN models. By combining CRE maps with gene expression data under specific conditions (e.g., drought), researchers can infer condition-specific GRNs and identify novel candidate regulators involved in stress responses (Pérez et al., 2024). Such multi-omics integration is essential for characterizing the complex regulatory landscape of the maize genome and for identifying functional elements that drive key agronomic traits.

5.2 Network construction and modeling methods

Network construction in maize GRN research often begins with correlation-based approaches, where coexpression patterns across large transcriptome datasets are used to infer potential regulatory relationships between transcription factors and their targets (Zhou et al., 2020). These methods can generate multiple GRNs representing different tissues, genotypes, or environmental conditions, each capturing distinct regulatory modules and biological processes (Zhou et al., 2020). The integration of coexpression networks with genetic mapping data, such as eQTLs, further supports the identification of causal regulators underlying important traits (Zhou et al., 2020).

Machine learning algorithms, such as GENIE3, have advanced GRN inference by leveraging large-scale RNA-Seq data to predict regulatory interactions without relying on high-quality position weight matrices (Huang et al., 2018). These approaches can model complex, non-linear relationships and are validated using independent datasets like ChIP-Seq. Recent developments also include graph neural networks for inferring GRNs from single-cell RNA-seq data, which improve accuracy and robustness in capturing cell-type-specific regulatory links (Figure 2) (Mao et al., 2023).

5.3 Key databases and bioinformatics tools

Publicly available databases and bioinformatics tools are critical for GRN research in maize. The maize tissue-specific GRN database (mGRN) provides access to tissue-level GRNs constructed from extensive RNA-Seq data, enabling researchers to query regulatory relationships and key transcription factors for specific tissues (Huang et al., 2018). This resource is complemented by open-source code and data repositories, facilitating reproducibility and further analysis.

Pan-genomic databases, such as MaizeGDB, integrate diverse genomic, transcriptomic, epigenomic, and structural variation data across multiple maize genomes, allowing users to track functional and structural differences at specific loci (Woodhouse et al., 2021). Tools like Camoco enable the integration of coexpression networks with GWAS data to prioritize candidate causal genes for complex traits, demonstrating the power of combining functional genomics and genetic association data for trait dissection and crop improvement (Schaefer et al., 2017).

6 Current Challenges and Future Perspectives

6.1 Limitations such as insufficient spatial and temporal resolution in existing studies

Despite significant advances in functional genomics and GRN construction in maize, current studies often lack sufficient spatial and temporal resolution. Most GRNs are derived from bulk tissue transcriptomics, which can obscure cell-type-specific regulatory interactions and dynamic changes during development or in response to environmental cues (Huang et al., 2018; Zhou et al., 2020). This limitation hampers the ability to fully understand how gene regulation varies across different tissues, developmental stages, and environmental conditions.

Additionally, while large-scale datasets and coexpression networks have identified many putative regulators, the precise timing and location of regulatory events remain difficult to resolve (Huang et al., 2018; Zhou et al., 2020). The lack of high-resolution data restricts the identification of transient or rare regulatory interactions, which are often critical for key developmental transitions and stress responses in maize.

6.2 Prospects of emerging technologies

Emerging technologies such as spatial omics and single-cell multi-omics offer promising solutions to overcome current limitations. Spatial omics approaches can map gene expression and regulatory element activity within the physical context of tissues, providing insights into how spatial organization influences gene regulation and trait development (Walley et al., 2016; Huang et al., 2018). These methods will enable researchers to dissect GRNs at unprecedented resolution, revealing new layers of regulatory complexity.

Single-cell multi-omics technologies, which simultaneously profile transcriptomes, epigenomes, and chromatin accessibility in individual cells, are poised to revolutionize maize functional genomics (Huang et al., 2018). By capturing cell-type-specific regulatory networks and dynamic changes during development or stress, these approaches will facilitate the discovery of novel regulators and interactions that are invisible in bulk analyses, advancing our understanding of maize biology.

6.3 The potential of functional genomics to accelerate molecular breeding and future research directions

Functional genomics holds great potential to accelerate molecular breeding by identifying key regulatory genes and networks underlying important agronomic traits (Xiao et al., 2017; Liu et al., 2019; Zhou et al., 2020). Integrating multi-omics data and advanced network modeling can pinpoint candidate genes for targeted breeding or genome editing, streamlining the development of high-yield, stress-resistant, and quality-improved maize varieties (Rodgers-Melnick et al., 2016; Xiao et al., 2017; Zhou et al., 2020). Open chromatin mapping and functional annotation further narrow the search for causal variants, making genomic selection and editing more efficient (Rodgers-Melnick et al., 2016).

Looking forward, future research should focus on expanding high-resolution, multi-omics datasets, developing robust analytical tools for GRN inference, and integrating functional genomics with breeding programs (Xiao et al., 2017; Liu et al., 2019). Collaborative efforts and community resources, such as reference genomes and public GRN databases, will be essential for translating functional genomics discoveries into practical crop improvement strategies.

7 Concluding Remarks

This study significantly advanced the research process of the maize gene regulatory system (GRN) by systematically analyzing the target sites of transcription factors and the physiological regulatory relationships they mediate. Based on the genome-wide gene expression profile, protein interaction analysis and multi-dimensional omics data integration, researchers have drawn a high-precision regulatory network map, revealing that the gene regulation of corn has characteristics such as dynamic response and regional coordination. These achievements have established theoretical models for analyzing how genetic regulation and epigenetic modification jointly affect growth and development as well as environmental adaptation mechanisms.

The regulatory network information analyzed based on genomics technology is of great value for the genetic improvement and basic research of corn. By locking in the core nodes that regulate the flowering cycle, stress resistance characteristics and material synthesis pathways, researchers have created new opportunities for molecular design breeding and genetic manipulation. Combining regulatory network data with genetic mapping and multi-omics not only improves the screening efficiency of genes related to superior traits, but also deepens the theoretical understanding of the phenotypic formation mechanism.

Genomics research techniques have provided a brand-new perspective for analyzing the composition and function of the maize gene regulatory system, promoting a dual breakthrough in plant science research. With the continuous development of high-precision omics methods and intelligent algorithms, the operation rules of regulatory networks will be analyzed more accurately in the future, and the targeted optimization of crop traits will be achieved. Subsequent research should focus on enhancing the spatio-temporal specificity analysis ability of tissues, integrating single-cell and three-dimensional omics technologies, and transforming theoretical achievements into efficient breeding plans to provide scientific and technological support for solving the global food supply problem.

Acknowledgments

Thank you to the anonymous peer review for providing targeted revision suggestions for the manuscript.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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