Cotton Genomics and Genetics 2025, Vol.16, No.5, 241-248 244 4 Expression Patterns and Regulatory Mechanisms of Key Enzymes 4.1 Spatiotemporal expression patterns of key enzymes during fiber maturation Not all enzymes are "online 24/7" during the maturation process of cotton fibers. The expression of different isomers of metabolic enzymes such as cytoplasmic pyruvate kinase often depends on specific tissues and specific developmental stages; in other words, they are on demand. They are not only controlled at the transcriptional level but also constrained by post-transcriptional mechanisms. The entire regulatory process is very detailed, with the aim of keeping up with the specific requirements of fibroblasts for energy and carbon flow at different maturation stages (Wulfert et al., 2020; Wang and Zhang, 2024). Under this dynamic mechanism, enzyme activity is actually constantly being "regulated" and "adjusted". 4.2 Effects of post-translational modifications on enzyme activities However, sometimes even if genes are expressed, enzymes may not take effect immediately. This involves the "switch" of the post-translation modification (PTM) layer. Modifications such as β -hydroxybutyrylation, acetylation or methylation of lysine can significantly alter the activity or stability of enzymes. For example, acyltransferase p300 can add a β -hydroxybutyryl group, and HDAC1/2 is responsible for removing them-these alterations may seem minor, but they are key actions in regulation, especially in scenarios of rapid switching of metabolic flux or stress response (Hemmerlin, 2013; Huang et al., 2021; Wang et al., 2022). Such modifications do not only act on the enzymes themselves, but may also be involved in a host of pathways such as DNA repair and metabolic regulation. 4.3 Comparative analysis of key enzyme expression among different cotton varieties In fact, even if they are all cotton, different varieties have quite different expressions of key enzymes. This is not merely a difference at the transcriptional level, but also involves the expression intensity and modification mode at the proteomic level. Studies have found that there are significant differences in the expression of some metabolic enzymes and regulatory enzymes among different cotton species, which directly affects the maturation rate and quality of fibers (Zanger and Schwab, 2013; Zelezniak et al., 2018). This kind of difference often stems from the underlying genetic variations and variations in regulatory networks. It is precisely for this reason that the combination of quantitative proteome data with genotype and phenotype information can more comprehensively depict the functional performance of different varieties and provide real and applicable references for breeding. 5 Case Studies: Functional Validation and Application of Candidate Genes 5.1 Expression verification of representative enzymes (e.g., GhCESA4, GhSUS) in cotton fibers To determine whether a gene is "truly useful", merely looking at its sequence is not enough; it is necessary to see if it has "come online" at the critical period. Genes such as GhCESA4 (cellulase synthase) and GhSUS (sucrose synthase), which are suspected to be involved in fiber maturation, are often confirmed by researchers for their expression through quantitative RT-PCR or transcriptome analysis. As long as they are active in a specific tissue or developmental stage, there is basically a reason to dig deeper (Figure 2) (Huang et al., 2021). 5.2 Gene knockdown (VIGS) and transgenic analyses reveal regulatory roles in fiber maturation A high level of expression doesn't necessarily mean functionality; one still needs to "give it a try". So, researchers often use some tools, such as VIGS (virus-induced gene silencing) or RNA interference, to first "turn off" a certain gene and see what the reaction is. Some choose to overexpress it or directly knock it out to observe whether it will affect the fiber length, strength or cell wall composition. If a certain trait changes, it is highly likely that this gene does have a regulatory role (Alinezhad et al., 2016; Rohde et al., 2018; Cornean et al., 2021). Nowadays, there are still many high-throughput in vivo systems and model organisms available. It is not a problem to screen a large number of genes at one time, and the efficiency is much faster than before (Zhu et al., 2017; Cornean et al., 2021). 5.3 Trait association studies and QTL integration for fiber quality improvement But verification is not enough. If these genes do not "play a role" in actual breeding materials, then they have no promotion value either. Therefore, many studies will combine functional verification with QTL analysis or GWAS. In this way, not only can it be confirmed that the gene is "properly expressed", but also whether it appears together with the ideal trait, which is known as "co-isolation" (Albert and Sauvage, 2022). If the genes screened out can be
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