Cotton Genomics and Genetics 2025, Vol.16, No.3, 107-116 http://cropscipublisher.com/index.php/cgg 110 openness). This approach allows researchers to find out which epigenetic state affects certain fiber-related genes, whether they are activated or inhibited, so as to have a more comprehensive understanding of the regulatory process during cotton fiber development (Ruprecht et al., 2024). For example, using high-resolution data to analyze chromatin openness and gene expression together can help reveal which epigenetic marks are related to gene activity, and also know their changing relationship in time and space (Zhang et al., 2023). 4.2 Predicting regulatory networks through multi-omics data integration In fact, at the beginning, many regulatory relationships are not clear when looking at transcriptome data alone. In addition, the information of the epigenome is often scattered, and there are many places where data is missing. Relying on one data type alone cannot give a full picture. At this time, putting different "omics" data together, such as transcriptome and epigenetic maps, can actually piece together a more complete picture. Although it is not easy to operate, some new computational methods, such as unsupervised learning tools such as scAI, can indeed integrate these sparse data and help us straighten out the previously chaotic signals (Kartashov and Barski, 2014; Jin et al., 2020). However, don't expect these methods to be in place in one step. What they can do is to provide a sense of direction. For example, where may there be key transcription factors? Which non-coding RNA has regulatory functions? Which areas are worth focusing on? These integrated analyses can pull clues out from a large number of complex signals. Although it cannot directly explain all the mechanisms, it does help us move one step forward in the matter of fibroblast differentiation (Bahrami et al., 2024). 4.3 Insights into stage-specific activation and repression of fiber genes Whether a gene is "on" or "off" is not entirely a matter of subjective guessing. Now there is a way to see this more clearly. When the two types of data, transcriptome and epigenome, are analyzed together, many patterns hidden behind the timeline can be seen. Which genes are turned on at which stage and when they are turned off can all be organized into a map. But there is a premise: these regulations are not necessarily simple and repetitive. Epigenetic mechanisms such as histone modification and DNA methylation have different combinations at different stages. Sometimes the impact is large, and sometimes the impact is not obvious. This is related to the "rhythm" of cotton fiber from the beginning, elongation to maturity (Lister et al., 2008; Marconett et al., 2013). In other words, understanding these stage changes is not just to satisfy scientific curiosity, it may also point to a very practical direction-we may be able to adjust fiber traits step by step to a better state through gene regulation. The key is that we must first understand "who is responsible and when". 5 Functional Modules and Regulatory Networks 5.1 Key hubs and modules identified through gene co-expression networks (WGCNA, etc.) To put it simply, the co-expression network is to pull genes that are "often expressed together" together to see if they are doing the same thing. Methods like WGCNA have indeed helped us dig out some interesting modules from thousands of genes. For example, a previous study divided more than 13 000 genes with different expressions into several groups according to tissue type. The MEblack module is relatively prominent. It is rich in genes related to cell wall formation and is also related to fiber length (Ma et al., 2024). However, not every module has a "commander". Genes like GhKCS1b_Dt and GhTUB5 are quite special. They play a significant role in controlling fiber elongation and are in a relatively core position in the entire network. There is also a point that is easily overlooked - in polyploid cotton, genes are not evenly divided. Studies have found that the contributions of the two subgenomes A and D to the network are not equal. In particular, some transcription factors in the A subgenome have more obvious regulatory effects (You et al., 2023; Xiong et al., 2024). 5.2 Signaling pathways involved in fiber elongation Whether the fiber can grow fast and well is not determined by a single signaling pathway. Auxin, ethylene, brassinolide-they are all involved. Through KEGG analysis, researchers found that some genes involved in fatty acid elongation and hormone signaling are regulated to varying degrees at different developmental stages (Liu et al., 2024). These changes are not "automatic", but are related to the participation of regulatory modules and specific transcription factors. For example, some modules are particularly sensitive to specific hormones and can control the expression of key genes in cotton fibers, thereby affecting subsequent developmental processes (Bao et al., 2023).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==