Cotton Genomics and Genetics 2025, Vol.16, No.1, 21-28 http://cropscipublisher.com/index.php/cgg 25 6.2 Multi-omics integration to improve breeding efficiency Metabolomics is not a one-man battle, it can also be used in conjunction with genomics, transcriptomics and proteomics. These omics data combined can help us understand the relationship between "genotype" and "traits" from more perspectives (Scossa et al., 2020). For example, which genes determine yield? Which are related to disease resistance and drought tolerance? These can be seen more clearly. Through these combined analyses, we can also find out more accurately "which gene is the problem", figure out which metabolic pathway is at work, and know how genes are regulated (Sharma et al., 2021). This method can also be combined with some high-throughput analysis tools to provide more clues for gene editing and breeding strategies, helping us develop better cotton varieties (Razzaq et al., 2022). 6.3 Construction of metabolomics-driven molecular breeding models Current research has also developed some metabolome-based breeding models. These models incorporate metabolic markers and pathway information to guide how to select ideal traits (Razzaq et al., 2022). In this way, we can screen out some cotton lines with great potential in the early stages of breeding. For example, varieties that are more resistant to stress, higher in yield, and more adaptable to climate change (Sakurai, 2022). Combining metabolomics and bioinformatics tools with some rapid breeding technologies can also speed up the development of new varieties. This is also particularly helpful for us to cope with various environmental challenges (Razzaq et al., 2019). 7 Case Studies 7.1 Improvement of compact cotton varieties based on metabolic traits Some studies have used metabolomics and transcriptomics to compare different cotton varieties, such as Zhongmian 50 (early maturing, compact) and Guoxin Cotton 11 (late maturing). The results showed that several metabolic pathways are particularly important, such as phenylpropanoid synthesis, tyrosine metabolism, and phenylalanine metabolism, which are all related to bud differentiation and early flowering. The study also noted that the content of phenolic acids decreases in early-maturing cotton. A gene called GhTYDC-A01 was also found to be related to flowering time. These results suggest that we can select more compact and faster-maturing cotton varieties by regulating metabolites and genes. 7.2 Functional validation of specific metabolites in regulating flower and boll distribution Functional studies have also found some specific metabolites and genes that are important for the development of cotton. For example, a study overexpressed the gene GhTYDC-A01 in Arabidopsis thaliana and found that the flowering time of these plants was delayed. This shows that this gene can indeed affect flowering time and is an important factor in regulating reproductive development. In addition, spatial metabolomics also helped to find several key metabolites that affect cotton fiber formation, such as linoleic acid, spermine and spermidine. Through gene knockdown or overexpression experiments, researchers confirmed that these metabolites play an important role in fiber cell initiation and development. 7.3 Pilot study on large-scale field screening using metabolomics data Metabolomics is now also beginning to be applied in field breeding. Researchers conducted a pilot study combining spatial metabolomics and lipidomics to analyze many different cottonseed varieties. They found 17 differential metabolites and 125 lipids related to stress resistance. These lipids can help plants remove reactive oxygen species and regulate osmotic pressure, which is very helpful for improving drought and salt tolerance (Liu et al., 2021). This shows that it is feasible to use metabolomics technology for screening in the field. We can select cotton varieties with good structure, high yield and strong stress resistance more quickly. 8 Concluding Remarks This study highlights the importance of metabolomics. It can help us find out which metabolites and metabolic pathways affect cotton plant type and yield. When we analyze metabolomics data together with genetic information and transcriptome data, we can find metabolic markers related to high yield, stress resistance, and ideal plant type. These findings allow us to judge the performance of varieties earlier, and to carry out breeding improvements more targetedly, accelerating the process of breeding high-performance cotton.
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