Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 262-268 http://cropscipublisher.com/index.php/tgg 267 Although these experiments are still in their early stages, they have already provided strong functional hints. Ultimately, these verifications give us more confidence to apply the predictions in the co-expression network to practical genes that can truly be used in wheat breeding. 7 Conclusion and Perspectives When studying the development mechanism of wheat ears, in the final analysis, it is still impossible to avoid a large number of gene regulatory factors that affect the shape and yield of the ears. In this study, through network analysis, we identified some co-expression modules and also found some key gene clusters closely related to spikelet formation and spike axis elongation. These things have not been mentioned for the first time, but now their dynamic changes during the inflorescence development process are a bit clearer. Although it cannot be said to have been fully mastered yet, at least the framework has been established, which has opened up new ideas for further in-depth understanding of the regulation of spike structure and can also be regarded as providing some molecular basis for yield improvement. But the problem is not small either. The genome of wheat is large enough and polyploid, with information piled up layer upon layer. The integration of omics data is by no means an easy task. The transcriptome data from different materials and at different time points vary significantly. Coupled with the fact that most of the candidate genes have not yet had time for functional verification, it is still hard to say whether many co-expression modules are truly useful at present. Not to mention the data at the epigenetic regulation, protein and metabolic levels, the current binding with the transcriptome is still far from sufficient, which leaves a piece of the puzzle missing in the regulatory map of the entire spike development. So, the next focus might have to be placed on cross-omics integration. Relying solely on a single data dimension is no longer far. By aggregating the information from the transcriptome, epigenome and genome, and combining it with a more mature network analysis model, it might be possible to build a more realistic regulatory map of spike development. Moreover, if these networks can be combined with CRISPR editing and functional verification experiments to advance simultaneously, there might be a real breakthrough in breeding. After all, the ultimate goal is still to transform these molecular-level understandings into the cultivation of wheat varieties with high yields and excellent ear shapes. The key lies in their implementation. Acknowledgments We thank Mr Z. Wu from the Institute of Life Science of Jiyang College of Zhejiang A&F University for his reading and revising suggestion. 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. References Ai G., He C., Bi S., Zhou Z., Liu A., Hu X., Liu Y., Jin L., Zhou J., Zhang H., Du D., Chen H., Gong X., Saeed S., Su H., Lan C., Chen W., Li Q., Mao H., Li L., Liu H., Chen D., Kaufmann K., Alazab K., and Yan W., 2024, Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat, Plant Communications, 5(5): 100879. https://doi.org/10.1016/j.xplc.2024.100879 Cao P., Fan W., Li P., and Hu Y., 2021, Genome-wide profiling of long noncoding RNAs involved in wheat spike development, BMC Genomics, 22: 493. https://doi.org/10.1186/s12864-021-07851-4 Dam S., Võsa U., Van Der Graaf A., Franke L., and Magalhães J., 2017, Gene co-expression analysis for functional classification and gene–disease predictions, Briefings in Bioinformatics, 19: 575-592. https://doi.org/10.1093/bib/bbw139 Gysi D., De Miranda Fragoso T., Zebardast F., Bertoli W., Busskamp V., Almaas E., and Nowick K., 2020, Whole transcriptomic network analysis using co-expression differential network analysis (CoDiNA), PLoS ONE, 15(10): e0240523. https://doi.org/10.1371/journal.pone.0240523 Hou J., Ye X., Feng W., Zhang Q., Han Y., Liu Y., Li Y., and Wei Y., 2022, Distance correlation application to gene co-expression network analysis, BMC Bioinformatics, 23: 46. https://doi.org/10.1186/s12859-022-04609-x
RkJQdWJsaXNoZXIy MjQ4ODYzNA==