Maize Genomics and Genetics 2025, Vol.16, No.4, 182-201 http://cropscipublisher.com/index.php/mgg 199 mutations or stress, the aleurone layer may not form correctly, which could damage the seed's ability to store and utilize nutrients. This tells us that these traits depend on timely gene activity, and the openness of chromatin is part of the control system, which ensures that everything occurs in the right sequence and position. Data further show that many differences in seed traits are tied to regulatory DNA, not the gene sequences themselves. We saw that SNPs linked to traits often sit in open chromatin areas. This means that both evolution and breeding may have worked on these regions, picking versions of DNA that keep chromatin more or less open at key spots, which affects how traits show up. For breeders, this is a useful insight. It’s a reminder to not just look at gene mutations, but also at nearby DNA that controls when those genes turn on. Even small changes in gene activity—driven by chromatin or nearby DNA elements—can cause big trait differences, like oil level or seed size. In practice, knowing how chromatin affects traits might make breeding more accurate. For example, adding markers from open chromatin regions to breeding models could help pick better plants. These markers likely matter because they control how genes behave. Single-cell ATAC-seq is another frontier that stands to revolutionize our understanding of seed development. The maize seed contains multiple tissues and cell types (embryo shoot, root, scutellum, various endosperm cell types, etc.), each with unique regulatory profiles. Our data, though high-resolution in time, was generally from whole-seed or bulk endosperm tissues. Single-cell ATAC-seq would allow us to parse out cell-type-specific chromatin landscapes. In a complex tissue like endosperm, this is particularly valuable – e.g., we could recover separate ATAC profiles for aleurone vs. starchy cells vs. BETL cells from a single assay. This would disentangle composite signals and possibly identify cell-type-specific enhancers that were diluted in bulk data. Encouragingly, techniques for single-cell ATAC in plants (including maize) are emerging. Coupling those with single-cell RNA-seq (which has already been applied to maize seeds for cell-type networks) would yield a powerful multi-omic atlas: for each cell type, know its chromatin accessibility and gene expression and how they change over development. Such a resource would immensely clarify how different seed compartments coordinate (for example, how the embryo “talks” to endosperm via chromatin changes and signaling molecules). The combined use of multiple omics tools can accelerate gene discovery and facilitate crop research. In the past, searching for the genes behind QTL (quantitative trait loci) was time-consuming and laborious, and it was difficult to determine which part of the genome was crucial. But now, with the help of methods such as ATAC-seq and other omics data, it is easier to identify useful genes or control regions. In our research, we discovered an enhancer within the QTL that controls the oil content. This region is associated with an AP2 transcription factor. As we collect more datasets - from different growth periods, stress environments and plant types - we can start to build large genetic networks. Machine learning can help complete this step. Tools like WGCNA can group genes with similar expressions. Similarly, ATAC-seq can identify DNA regions that are open or closed to each other. These patterns might also reveal the three-dimensional folding patterns of the genome, especially when folding occurs under stress. So far, such comprehensive data have been used to construct the genetic maps of Arabidopsis roots and corn leaves. If we apply the same method to seeds, it may help us understand seed dormancy and how seeds store nutrients. Acknowledgments I would like to express my gratitude to the reviewers for their valuable feedback, which helped improve the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bernardi J., Battaglia R., Bagnaresi P., Lucini L., and Marocco A., 2019, Transcriptomic and metabolomic analysis of ZmYUC1 mutant reveals the role of auxin during early endosperm formation in maize, Plant Science, 281: 133-145. https://doi.org/10.1016/j.plantsci.2019.01.027 Bubb K., Hamm M.O., Tullius T.W., Min J.K., Ramirez-Corona B., Mueth N.A., Ranchalis J., Mao Y.Z., Bergstrom E.J., Vollger M.R., Trapnell C., Cuperus J.T., Stergachis A.B., Queitsch C., and Stergachis A., 2024, The regulatory potential of transposable elements in maize, bioRxiv, 602892: 1-33. https://doi.org/10.1101/2024.07.10.602892
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