Triticeae Genomics and Genetics, 2025, Vol.16, No.3, 138-147 http://cropscipublisher.com/index.php/tgg 144 regulatory networks is also involved. For instance, transcription factors like TaABI3-B1 are a crucial part of it. They regulate downstream genes and ultimately affect not only the size of the grains but also the overall efficiency of grain-filling. As for whether these pathways are useful or not, genome-wide association analyses and QTL studies have long provided the answer: they have direct contributions to both yield and processing quality (Li et al., 2023; 2024; 2025). 6.2 Spatiotemporal expression basis of protein synthesis and processing quality Whether the gluten in wheat is strong or not and whether the protein content is sufficient, in the final analysis, it is still a matter of the "timely and punctually" expression of genes. Especially for those high-molecular-weight glutenin subunits and storage protein genes, the expression time and spatial location are very carefully selected, and their activity is most obvious in the endosperm layer and aleurone layer. Meta-QTL and transcriptome integration analysis has helped us identify a number of candidate genes and regulatory elements that affect the accumulation rate and intensity of proteins, and some of these variations can explain the significant differences in bread quality (Gudi et al., 2022). The value of spatial transcriptome maps is not just for show; it can help breeders identify key marker genes and modules for subsequent quality improvement. 6.3 Formation of secondary metabolites and nutritional quality It is not only starch and protein that determine the nutritional value of grains. Secondary metabolic pathways such as amino acid synthesis and arginine metabolism are equally important. The expression of related genes is actually quite "selective". At different tissues and developmental stages of the grain, they each have their own positions and time points. It is precisely these specific expression patterns that determine which regions can accumulate more nutritious or biologically active compounds. That is to say, whether the health value is high or not sometimes does not depend on the total amount but on the distribution. To identify these genes, traditional methods alone are insufficient. Spatial transcriptome combined with QTL mapping is the key breakthrough (Li et al., 2023; 2025). Such achievements provide practical paths and targets for improving the nutritional quality of wheat during the breeding process. 7 Application Value of Transcriptome Atlas in Wheat Genetic Improvement 7.1 Application potential of key regulatory genes during the grain filling stage in molecular breeding Ultimately, whether the yield and quality of wheat can be improved during the grain-filling stage depends on whether there are precise control targets. And the spatio-temporal transcriptome atlas precisely provides quite a few clues for this matter. The expression patterns of genes involved in starch synthesis, protein accumulation, or coping with adversity at different tissue and developmental stages have long been depicted more and more clearly. Some key genes have passed functional verification and have become key targets in molecular breeding. Not every candidate gene is worth a try, but those that are highly expressed in specific tissues or time periods can indeed be given priority. Breeders can directly utilize these expression data for marker-assisted selection and even gene editing to rapidly cultivate wheat varieties with strong grain-filling capacity and stable quality (Dong et al., 2015; Cao et al., 2020). 7.2 Integration with quantitative trait loci (QTL) mapping and genomic selection Of course, relying solely on transcriptome data is not enough. Only by combining it with QTL can the positioning accuracy be guaranteed. Especially when it comes to complex traits like grain weight and plant height, it is difficult to explain clearly from a single data source. The current approach is usually to integrate transcript abundance with consensus maps and SNP data to identify more stable and reliable QTLS and candidate genes in different planting environments and genetic backgrounds (Cao et al., 2020; Qu et al., 2021). On the other hand, incorporating transcriptome information into genomic selection models has indeed enhanced the accuracy of predicting the genetic value of certain traits, especially in controlled environments. Of course, this method is not "cheap". Large-scale analysis is costly and not easy to operate. Therefore, many teams are now beginning to explore another direction - integrating environmental variables and genomic information as a more realistic and practical alternative strategy (Liu et al., 2024).
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