Cotton Genomics and Genetics 2025, Vol.16, No.6, 278-289 http://cropscipublisher.com/index.php/cgg 285 but in "when to respond". Only when the response is early and the amplitude is large can there be a chance to maintain the stability of the proteome and immune balance. 6.2 Key metabolic and signaling differences The differences at the metabolic level are equally distinct. Resistant varieties often voluntarily sacrifice a portion of their photosynthesis. The down-regulation of RuBisCO subunits and oxygen-releasing enhancers indicates that they shift energy from growth to defense (He et al., 2022). Meanwhile, the activities of enzymes such as phenylalanine aminase (PAL), cinnamyl alcohol dehydrogenase (CAD), and caffeyl coA O-methyltransferase (CCoAOMT) increased significantly. These enzymes directly participate in lignin synthesis, reinforce vascular tissue, and prevent pathogen invasion (Xiong et al., 2021b). Histochemical tests also confirmed that the resistance of Hai 7124 and Zhongzhi Mian No. 2 is closely related to their stronger lignification. At the level of signal regulation, resistant varieties have stronger "coordination". Salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) related pathways are active, forming a tight regulatory network with each other, which can not only trigger systemic acquired resistance (SAR), but also maintain local defense. Calcium signaling molecules, such as calmodulin (CaM) and calcium-dependent protein kinase (CDPK), cooperate with the MAPK cascade reaction to jointly amplify defense signals (Zhang et al., 2024). However, susceptible varieties seem to be "disconnected" at this stage. The crosstalk between the SA and JA pathways is relatively weak, and the activation of transcription factors such as WRKY, MYB, and NAC is insufficient, leading to the interruption of the signal transduction chain and a slow response of defense genes. This is also the fundamental reason why it is prone to getting out of control after infection. 6.3 Breeding implications This difference is not merely a theoretical issue but a starting point for disease-resistant breeding. The defense proteins revealed by the comparative proteome, such as PR protein, antioxidant enzymes, and enzymes related to phenylpropanin metabolism, are all ideal molecular markers for screening resistant materials. For instance, overexpression of the lignin-related laccase gene GhLAC15 or the phenylalanine enzyme Gh4CL30 can significantly enhance the resistance of cotton, enabling it to maintain a higher lignification level and antioxidant capacity under the infection of Wilt yellow. If proteomic data can be combined with transcriptomic and metabolomic results, more quantitative trait loci (QTLS) and regulatory networks related to resistance can be located, providing more precise targets for molecular breeding. In the future, molecular marker-assisted selection (MAS) based on multi-omics fusion will be combined with CRISPR/Cas gene editing, opening up a new path for breeding broad-spectrum and long-lasting disease-resistant cotton varieties. The ultimate goal is not only to enhance resistance, but also to free cotton from its reliance on chemical control and achieve a more sustainable production method. 7 Future Directions in Cotton Proteomic Research 7.1 Integrative omics approaches The research on cotton proteomics seems to have reached a stage where "studying alone is not sufficient". True breakthroughs often occur in its combination with other omics, such as genomics, transcriptomics, metabolomics, phosphorylomics and even epigenetics. When these data are integrated together, a more complete molecular picture can be pieced together to explain the resistance mechanism of cotton to the Variegata. Proteomics is good at capturing changes, especially the dynamic responses of proteins under stress. However, if it is compared with the results of the transcriptome or metabolome, it is possible to distinguish which are the results of transcriptional regulation and which are the offsets caused by post-translational modifications, thereby obtaining a more comprehensive understanding. In recent years, platforms such as the "Multi-omics Database of Wheel-Plant Interactions (PPI-MD)" have provided valuable examples: when genomic, proteomic and metabolomic data are placed under the same analytical framework, key defense regulatory factors tend to be more easily identified (Shi et al., 2025).
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