Bioscience Methods 2025, Vol.16, No.2, 108-116 http://bioscipublisher.com/index.php/bm 112 5.2 Application of proteomics and metabolomics in studying disease resistance mechanisms Proteomics and metabolomics provide a new perspective for studying the pathogenesis of sugarcane. Proteome studies have identified a variety of differentially expressed proteins (DEPs) that play an important role in disease resistance, including defense-related proteins that deal with pathogens such as Sporisorium scitamineum and Acidovorax avenae (Su et al., 2016). For example, there are significant expression differences in proteins related to metabolic processes, stress responses, and defense in disease-resistant and sensory sugarcane varieties, which may become potential disease-resistant biomarkers (Singh et al., 2019; Zhou et al., 2021). Although metabolomics is less studied, it has a complementary role in revealing the metabolic pathways activated during pathogen infestation, helping to further understand the biochemical basis of disease resistance. 5.3 Multi-omics data-driven strategies for disease-resistant breeding Integrating multiomic data such as genome, transcriptome, proteome and metabolomic is the key to formulating efficient disease-resistant breeding strategies. Through multidimensional data integration, researchers can identify a series of core disease-resistant genes, regulatory pathways and biomarkers, thereby providing strong support for breeding programs (Chen et al., 2024). For example, the integration of genomic and transcriptomic data plays an important role in identifying molecular markers associated with resistance to puff blight (Pokkah Boeng) and sugarcane yellow leaf virus (SCYLV), providing a key tool for marker-assisted selection (MAS) (Pimenta et al., 2021; Lin et al., 2024). In addition, a genomic prediction model combining machine learning and feature selection improves the accuracy of prediction of disease-resistant traits, providing a practical solution for the breeding of disease-resistant sugarcane varieties (Islam et al., 2021; Pimenta et al., 2023). These multiomic strategies not only help to have a deep understanding of the pathogenesis of sugarcane, but also accelerate the cultivation process of strongly resistant sugarcane varieties (Li, 2024b). 6 Prospects of Emerging Technologies in Sugarcane Disease-Resistant Breeding 6.1 Potential applications of gene editing technologies Gene editing technology, especially CRISPR/Cas9, is innovating the field of plant disease-resistant breeding. Compared with traditional gene editing tools such as megnucleases, zinc finger nucleases (ZFNs) and transcriptional activator-like effector nucleases (TALENs), CRISPR/Cas9 has the advantages of simple design, high success rate, wide application range and low cost, so it is more popular (Borrelli et al., 2018; Boubakri, 2023). This technology can accurately modify the plant genome, and cultivate disease-resistant crops by targeting and regulating susceptible genes. For example, CRISPR/Cas9 has been successfully applied to crops such as rice, tomato and wheat to enhance their resistance to virus, fungal and bacterial diseases (Park et al., 2024). In sugarcane breeding, the potential of CRISPR/Cas9 lies in its ability to create disease-resistant varieties that do not require genetically modified, which is of great significance for achieving sustainable agricultural development (Ahmad et al., 2020). However, gene editing technology also has many limitations (Figure 2). 6.2 Prospects of pan-genomics and single-cell omics in disease resistance research Researchers are applying new approaches called pangenomics and single-cell omics to investigate how plants defend against disease. The approaches enable the comparison of genetic variation among plant species and the identification of genes that enhance crop disease resistance. Researchers also integrate various approaches-screening genes, proteins, and metabolites in plants-to understand how crops react to environmental stressors (Razzaq et al., 2021). Utilization of these techniques in combination with the CRISPR gene-editing tool enables the study of particular genes and enhances crop resistance to pathogens. This strategy has the potential to develop novel and improved ways of safeguarding sugarcane from infection. The new technologies give researchers an insight into plant defense that was not possible with the previous techniques. 6.3 Application of artificial intelligence and big data in predicting disease resistance genes Artificial intelligence and big data are transforming learning on plant breeding, particularly the identification of disease-resistant genes. Such emerging technologies can analyze vast amounts of plant trait and genetic data and identify patterns that human methods previously overlooked. Smart computer algorithms search difficult data to make informed guesses about how genes interact with one another, thereby making it simpler for scientists to
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