Cotton Genomics and Genetics 2025, Vol.16, No.1, 21-28 http://cropscipublisher.com/index.php/cgg 23 2.3 Role of endogenous hormones in the establishment of plant architecture Cotton's own hormones also play a big role in regulating plant shape. For example, there is a regulatory module called miR164-GhCUC2-GhBRC1, which can control the development of lateral buds and the growth of branches by affecting the signal of abscisic acid (ABA). Among them, GhBRC1 can activate the synthesis of ABA, thereby reducing branching (Sun et al., 2021; Zhan et al., 2021). There is also a bHLH transcription factor called GhPAS1, which can regulate brassinosteroids (BR) to promote cell elongation and whole plant growth (Wu et al., 2021). Auxin is also critical. For example, GhHB12 is involved in this signaling pathway, affecting the height and number of branches of cotton (Liu et al., 2022). In addition, gibberellin (GA) is also involved in regulation. For example, the gene GhSBI1 can regulate the length of internodes. It also interacts with DELLA proteins to affect cell elongation and GA signaling (Zhong et al., 2024). These hormones will influence each other, integrate genetic and environmental signals together, and finely regulate the structure of cotton. In this way, cotton can grow high yields and adapt to different environments. 3 Advances in Metabolomics Research in Cotton 3.1 Establishment and integration of cotton metabolomics databases In recent years, there are more and more metabolomics resources for cotton. Researchers have collected metabolic data at different developmental stages and different parts, and also integrated transcriptome data. For example, a platform called Cotton Metabolism Regulatory Network (CMRN) was established in the study, which contains more than 2 100 metabolites and more than 90 000 genes. These data have provided great help for everyone to study the growth, structure and yield of cotton (Liu et al., 2024). There are also some databases and visualization tools that specifically collect information on cotton ovules and fiber development. These tools make it easier for researchers to share data and do comparative analysis. 3.2 Association analysis of key metabolic pathways and high-yield traits Scientists analyzed the metabolome and transcriptome data together and found that some metabolic pathways are particularly related to the high-yield traits of cotton. For example, pathways such as phenylpropanoid synthesis, tyrosine metabolism, and phenylalanine metabolism are related to cotton bud differentiation and flowering time. A gene called GhTYDC-A01 was found to affect these processes. In the process of cotton fiber development, another class of substances called very long chain fatty acids (VLCFA) is also critical. They are synthesized through the fatty acid elongation pathway. And genes like GhKCS1b_Dt are important regulators in this process (Liu et al., 2024). In addition, during the somatic embryonic development of cotton, scientists found that purine metabolism and flavonoid synthesis are also changing. This suggests that they may affect how cells differentiate and regenerate (Guo et al., 2019). 3.3 Mining and functional validation of targeted metabolites Now, through metabolomics, scientists can more accurately find which metabolites affect cotton growth, stress resistance, and yield. Different accumulation levels of substances such as phenolic acids, flavonoids, and amino acids will affect the early maturity of cotton, fiber quality, and response to environmental stress (Han et al., 2023; Liu et al., 2024). The researchers also verified the function of genes. For example, after overexpressing or silencing candidate genes such as GhTYDC-A01 or GhKCS1b_Dt, it was found that they can really affect flowering time and fiber elongation. These research results show that we can optimize the structure of cotton and increase yield through the method of "targeted metabolic regulation". This is a very promising path for breeding and metabolic engineering. 4 Metabolomics-Driven Strategies for Optimizing Plant Architecture 4.1 Screening for ideal plant types based on metabolic characteristics Now, researchers can use large-scale metabolite analysis methods to find metabolic characteristics related to good cotton structure and yield (Hong et al., 2016). If the metabolome data is combined with genetics and transcriptome, key genes can be located more accurately. In this way, subsequent functional verification and screening can also be done faster (Kumar et al., 2017; Shen et al., 2022). This method helps us find cotton varieties with suitable plant types and good metabolic performance more quickly. Especially in breeding, it can save a lot of time and energy.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==