Cotton Genomics and Genetics 2025, Vol.16, No.1, 21-28 http://cropscipublisher.com/index.php/cgg 26 However, there are still many difficulties in using metabolomics in cotton breeding. First of all, the genome of cotton is very complex, it is polyploid, and there are many duplicated genes. This makes it difficult for us to find those truly useful metabolic genes at once. In addition, we still lack a standardized metabolic database specifically prepared for cotton. Many of the data used now are scattered and not very unified. Another point is that the metabolome and other "omics" data (such as genome and proteome) have not been integrated well. If these data can be combined better, we can see more comprehensively how various traits come from. In addition, if you want to use a good method developed in the laboratory in the field, you have to try it several times. Not only do you need to improve the method itself, but you also need to verify it repeatedly under different environments to know whether it is stable and reliable. Although there are still challenges, metabolomics still has great prospects in cotton breeding. Today's high-throughput analysis technology is getting faster and faster, and the integration technology between different "omics" is becoming more and more mature. These advances can help us save time and improve efficiency in breeding. In the future, we can combine metabolome data with genome and transcriptome data, and use gene editing tools such as CRISPR/Cas to more quickly identify key pathways that control complex traits. In this way, we can build a metabolome-driven breeding model, and then use some rapid breeding methods to breed cotton varieties that are adaptable to climate change and have high yields. With these means, cotton production can also go more steadily and further in the face of global climate change. Acknowledgments We thank Mr Z. Tao 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 An J., Zhang Z., Li X., Xing F., Lei Y., Yang B., Wang Z., Han Y., Chen H., Wang G., Feng L., Du W., and Li Y., 2022, Loose and tower-type canopy structure can improve cotton yield in the Yellow River basin of China by increasing light interception, Archives of Agronomy and Soil Science, 69(6): 920-933. https://doi.org/10.1080/03650340.2022.2045584 Fernie A., and Schauer N., 2009, Metabolomics-assisted breeding: a viable option for crop improvement? Trends in Genetics, 25(1): 39-48. https://doi.org/10.1016/j.tig.2008.10.010 Guo C., Bao X., Sun H., Zhu L., Zhang Y., Zhang K., Bai Z., Zhu J., Liu X., Li A., Dong H., Zhan L., Liu L., and Li C., 2024, Optimizing root system architecture to improve cotton drought tolerance and minimize yield loss during mild drought stress, Field Crops Research, 308: 109305. https://doi.org/10.1016/j.fcr.2024.109305 Guo H., Guo H., Zhang L., Tang Z., Yu X., Wu J., and Zeng F., 2019, Metabolome and transcriptome association analysis reveals dynamic regulation of purine metabolism and flavonoid synthesis in transdifferentiation during somatic embryogenesis in cotton, International Journal of Molecular Sciences, 20(9): 2070. https://doi.org/10.3390/ijms20092070 Han M., Cui R., Wang D., Huang H., Rui C., Malik W., Wang J., Zhang H., Xu N., Liu X., Lei Y., Jiang T., Sun L., Ni K., Fan Y., Zhang Y., Wang J., Chen X., Lu X., Yin Z., Wang S., Guo L., Zhao L., Chen C., and Ye W., 2023, Combined transcriptomic and metabolomic analyses elucidate key salt-responsive biomarkers to regulate salt tolerance in cotton, BMC Plant Biology, 23(1): 245. https://doi.org/10.1186/s12870-023-04258-z Hong J., Yang L., Zhang D., and Shi J., 2016, Plant metabolomics: an indispensable system biology tool for plant science, International Journal of Molecular Sciences, 17(6): 767. https://doi.org/10.3390/ijms17060767 Huang X., Liu H., and Ma B., 2022, The current progresses in the genes and networks regulating cotton plant architecture, Frontiers in Plant Science, 13: 882583. https://doi.org/10.3389/fpls.2022.882583 Iqbal A., Jing N., Qiang D., Wang X., Gui H., Zhang H., Pang N., Zhang X., and Song M., 2022, Physiological characteristics of cotton subtending leaf are associated with yield in contrasting nitrogen-efficient cotton genotypes, Frontiers in Plant Science, 13: 825116. https://doi.org/10.3389/fpls.2022.825116 Ji G., Liang C., Cai Y., Pan Z., Meng Z., Li Y., Jia Y., Miao Y., Pei X., Gong W., Wang X., Gao Q., Peng Z., Wang L., Sun J., Geng X., Wang P., Chen B., Wang P., Zhu T., He S., Zhang R., and Du X., 2020, A copy number variant at the HPDA-D12 locus confers compact plant architecture in cotton, New Phytologist, 229(4): 2091-2103. https://doi.org/10.1111/nph.17059
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