Molecular Pathogens 2024, Vol.15, No.4, 179-188 http://microbescipublisher.com/index.php/mp 185 6 Applications of Transcriptomic Data in Wheat Breeding 6.1 Use of transcriptomics to identify disease-resistant cultivars Transcriptomic data has proven invaluable in identifying disease-resistant wheat cultivars. By analyzing the expression profiles of genes under various stress conditions, researchers can pinpoint differentially expressed genes (DEGs) associated with disease resistance (Wang and Li, 2024). For instance, a study identified 194 DEGs linked to multiple disease resistances, including septoria tritici blotch, fusarium head blight, and karnal bunt, among others (Babu et al., 2020). Similarly, transcriptome profiling has been used to compare resistant and susceptible wheat cultivars under stress conditions, revealing specific genes that contribute to resistance (Konstantinov et al., 2021). These insights enable breeders to select cultivars with enhanced resistance traits, thereby improving the overall resilience of wheat crops. 6.2 Integrating transcriptomic data with traditional breeding methods Integrating transcriptomic data with traditional breeding methods enhances the precision and efficiency of developing disease-resistant wheat varieties. Traditional breeding often relies on phenotypic selection, which can be time-consuming and less accurate. However, the incorporation of transcriptomic data allows for the identification of genetic markers linked to resistance genes, facilitating marker-assisted selection (MAS). For example, RNA-Seq bulked segregant analysis has been employed to identify high-resolution genetic markers for yellow rust resistance, which can be used in MAS to expedite the breeding process (Ramírez-González et al., 2015). Additionally, the use of genomic selection (GS) models, which incorporate transcriptomic data, has shown high prediction accuracies for resistance traits, further streamlining the breeding process (Pang et al., 2021). 6.3 Potential for developing transgenic wheat with enhanced resistance The potential for developing transgenic wheat with enhanced disease resistance is significantly bolstered by transcriptomic insights. By identifying key resistance genes and understanding their expression patterns, researchers can engineer wheat varieties with improved resistance profiles. Advances in genomics and bioinformatics have facilitated the cloning and functional characterization of resistance (R) genes, which can be introduced into wheat cultivars to confer resistance against a broad spectrum of pathogens (Hafeez et al., 2021). For instance, the creation of a wheat resistance gene atlas aims to catalog and deploy R genes efficiently, providing a robust framework for developing transgenic wheat with durable resistance to major diseases such as rusts, blotch diseases, and powdery mildew. This approach not only enhances the resistance of wheat but also contributes to global food security by mitigating yield losses due to diseases. 7 Concluding Remarks Recent transcriptomic studies have significantly advanced our understanding of wheat's defense mechanisms against various pathogens. For instance, the identification of differentially expressed genes (DEGs) and proteins in wheat's response to stripe rust has highlighted the role of transcriptional regulation in activating resistance-related genes. Similarly, comparative transcriptome analyses have revealed distinct gene expression profiles associated with resistance or susceptibility to Wheat Dwarf Virus (WDV), emphasizing the importance of specific transcription factors and regulatory networks. Additionally, the study of leaf rust resistance has shown that genes such as Lr34 and Lr67 are crucial in providing long-term protection through adult plant resistance (APR) mechanisms. These insights collectively underscore the complexity and specificity of wheat's transcriptomic responses to pathogen attacks. Future research should focus on several key areas to further enhance our understanding and application of transcriptomics in wheat disease resistance. First, there is a need for more comprehensive studies that integrate transcriptomic data with proteomic and metabolomic analyses to provide a holistic view of the defense mechanisms. Second, exploring the regulatory networks and interactions between different resistance genes and their associated pathways can offer deeper insights into the molecular basis of resistance. Third, the development of advanced bioinformatics tools and databases will be essential for the effective analysis and interpretation of
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