Molecular Microbiology Research 2024, Vol.14, No.6, 277-289 http://microbescipublisher.com/index.php/mmr 285 6.2 Pyramiding resistance genes Gene pyramiding is a strategy that involves stacking multiple resistance genes into a single rice variety to achieve broad-spectrum and durable resistance against blast disease. Some studies have shown that the resistance rate of spike canker with three resistance genes increased by 54.3% compared with one gene, but more studies have concluded that there is no significant negative correlation between the number of resistance genes carried and resistance to rice blast, and that the polymerization of resistance genes is not a simple superposition of resistance spectra, and that the polymerization of genes with good resistance and complementary spectrums of resistance should be selected, which not only broadens the spectrum of resistance, but also improves the resistance to some physiological microspecies. The studies showed that the correlation between the number of resistance genes carried and the resistance to spikelet plague was not significant. For the improvement of resistance to spikelet plague, in addition to the simple polymerization of resistance genes, the strengths and weaknesses of resistance genes and the interactions between genes in different genetic backgrounds should be taken into full consideration. (Wu et al., 2023). This approach has been successfully implemented using MAS to combine genes such as Pi2, Pi9, and others, resulting in rice lines with enhanced resistance to multiple biotic stresses (Ludwików et al., 2015). For example, the pyramiding of Pi9 and Xa23 genes in the GZ63S line has led to significant improvements in resistance to both blast and bacterial blight (Ni et al., 2015). Additionally, the combination of Piz5 and Pi54 genes in the PRR78 line has shown promising results in developing blast-resistant Basmati rice (Singh et al., 2013). The effectiveness of gene pyramiding is further supported by the successful integration of up to five blast-resistance genes in japonica rice varieties, providing a robust defense against diverse strains of the pathogen (Zampieri et al., 2023). 6.3. Integration of modern biotechnologies (CRISPR, Genomics) The advent of modern biotechnologies such as CRISPR and advanced genomic tools has revolutionized the field of rice breeding. CRISPR/Cas9 technology allows for precise genome editing, enabling the targeted modification of blast resistance genes to enhance their effectiveness. This technology has the potential to introduce novel resistance genes or enhance existing ones, thereby providing a powerful tool for developing blast-resistant rice varieties (Miah et al., 2013). Genomic selection, which involves the use of genome-wide markers to predict the performance of breeding lines, has also been employed to accelerate the development of resistant varieties. The integration of these biotechnologies with traditional breeding methods and MAS can significantly enhance the efficiency and precision of breeding programs aimed at combating blast disease (Das et al., 2017; Fukuoka, 2018). However, the cycle of selecting disease-resistant varieties through traditional breeding methods is very long and labor-intensive for phenotypic characterization. In general, it takes at least 6 years to improve disease resistance in an old variety, and the identification of rice blast resistance is time-consuming and labor-intensive, and lacks a certain degree of accuracy through direct phenotyping. Mining disease resistance genes and utilizing them through molecular marker-assisted selection is an effective way to rapidly improve varietal disease resistance and extend the life of old varieties (Sang et al., 2022) 7 Challenges and Future Directions 7.1 Technical and logistical challenges The exploration of genetic diversity for blast resistance in Dian-type hybrid and upland rice faces several technical and logistical challenges. One significant challenge is the complexity of the rice genome and the polygenic nature of blast resistance, which involves multiple genes and quantitative trait loci (QTLs) (Ballini et al., 2008; Tan et al., 2022). The identification and mapping of these genes require advanced molecular techniques and high-throughput sequencing, which can be resource-intensive and time-consuming (Ashkani et al., 2015; Ning et al., 2020). Additionally, the integration of resistance genes into elite cultivars through breeding programs is complicated by the need to maintain other desirable agronomic traits, such as yield and quality (Xu et al., 2008). The variability in pathogen populations and the emergence of new virulent strains further complicate the breeding process, necessitating continuous monitoring and updating of resistance genes (Nizolli et al., 2017; Herawati et al., 2022).
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