Cotton Genomics and Genetics 2025, Vol.16, No.5, 241-248 246 6.2 Integration of proteomic, phenotypic, and genotypic data for genetic improvement Sometimes, merely looking at DNA sequences is not intuitive enough, especially when it comes to proteins that function only after translation. Proteomics fills this "blind spot", as it can help us discover modifications or abundance changes that have not been detected at the RNA level. If the protein data, phenotypic expression and genotype information are analyzed together, many previously unclear trait mechanisms will become much clearer. In this way, breeders can not only know where a certain trait "grows", but also understand the "regulatory logic" behind it, and then precisely select species (Das et al., 2015; Adnane et al., 2024). This kind of multi-omics combination approach has been attempted in the fields of animals and plants in recent years, and the feedback at the practical level has become increasingly positive. 6.3 Conceptual design of proteome-assisted selection (PAM) for precision breeding The concept of proteomic assisted selection (PAM) has not been around for long, but it fills an important "gap" in traditional methods. In the past, we relied more on genotype and phenotype for seed selection. Now, if we can directly find clues from the proteome spectrum, it might be faster and more accurate. The core of PAM is to identify individuals with expression patterns that are strongly related to the target trait at the population level. It does not replace the existing methods but serves as a supplement-especially when phenotypic differences are linked to changes in protein function, this strategy is more advantageous (Das et al., 2015; El-Hack et al., 2018; Adnane et al., 2024). As the integration of proteomics data and other omics deepens, this approach may play a more direct role in ensuring food security and promoting breeding efficiency. 7 Conclusions and Outlook It is difficult to figure out how cotton fibers mature just by omics alone. In recent years, the research on quantitative proteomics, genomics and transcriptomics has made a big step forward-genes like ACLA-1, VTC2 and GA2OX1 were screened out in such multi-dimensional data. These are all highly expressed during the critical period of fiber development, and they seem to be related to early maturity and fiber quality. The problem is that the genetic relationships among these traits are not always on the same side; sometimes they even interfere with each other. So, even though there are already some major QTLS and candidate genes, it is still not very easy for breeding strategies to balance yield, early maturity and quality. However, multi-omics integration has at least provided us with some clues behind fiber maturation, such as how dynamic processes like cell wall biosynthesis, hormone signaling, and cytoskeletal regulation are intertwined. However, to be fair, proteomics is not that easy to handle in plants. Especially at the mature stage of cotton fibers, where the cell walls are thick and crystalline, extracting proteins is like picking water out of a stone. Not only is the yield low, but it is also very easy to lose those key regulatory proteins. Moreover, cotton itself is not a species with a simple genome. Polyploids and highly similar gene families all make the recognition and quantification of proteins complex. What's more troublesome is that once in the fields, the variations between different environments and the interaction between genotypes and the environment also make the interpretation of proteomic data less stable. Looking ahead, it's not over yet. If proteomics is truly to play a greater role in the genetic improvement of cotton, it must be carried out in conjunction with genomics, transcriptomics, and phenomics. Multi-omics integration is a major trend. Only by piecing together these data can we more accurately identify those biomarkers and regulatory networks that are truly controlling fiber maturation and quality. New tools such as single-cell omics and high-throughput phenotyping techniques may also offer finer resolutions in the future. Combined with gene editing, perhaps we can breed a batch of new cotton varieties that not only have good fibers but also are resistant to environmental fluctuations. Acknowledgments We appreciate Dr Zhou from the Hainan Institution of Biotechnology for his assistance in references collection and discussion for this work completion.
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