Legume Genomics and Genetics 2025, Vol.16, No.3, 128-134 http://cropscipublisher.com/index.php/lgg 132 found that when diseases such as anthracnose and bacterial blight occur, the NB-LRR gene in resistant strains is significantly upregulated, while the response in susceptible strains is much weaker or even inconsistent. For example, the gene Phvul.001G134500 has a strong induced expression in the context of disease resistance (Figure 2) (Wu et al., 2017). These results once again demonstrate that using transcriptome methods to screen candidate disease-resistant genes is indeed of great reference value. Figure 2 Chromosomal distribution of the NBS-LRR genes in the common bean. The gray bars represent all 11 chromosomes in the common bean. Boxes across each bar designate the location of each gene. The cluster number is shown at the top of each cluster (Adopted from Wu et al., 2017) 6.2 qRT-PCR validation and tissue-specific expression of candidate resistance genes RNA-Seq alone is not enough. Subsequent qRT-PCR verification is crucial, especially for confirming the expression patterns of NB-LRR genes that are sensitive to pathogen responses. Experiments on kidney beans have shown that some genes are rapidly upregulated in resistant materials when exposed to disease stimuli, and this is not a systemic response but rather concentrated in certain tissues, such as "frontline" sites like leaves and pods. This tissue-specific expression indicates that they are likely to directly participate in the local defense response, and also indirectly emphasizes that the results of RNA-Seq cannot be directly used and need to be further verified by quantitative means (Wu et al., 2017). 6.3 Functional annotation and protein structure modeling for mechanism prediction Of course, identifying candidate genes is just the beginning. To understand exactly what they do, we still need to look at the results of functional annotations and protein modeling. In the research of common kidney beans, the NS-LRR genes associated with known resistance loci (such as NSSR24, NSSR65, NSSR73, NSSR260, NSSR265) have been clearly located, and the protein domains have also been analyzed in depth (Wu et al., 2017). Through these models, combined with phylogenetic relationships and conserved motifs, it is possible to infer how they might recognize the effector proteins of pathogens and initiate immune pathways. This type of analysis has great guiding significance for subsequent functional verification and selection of breeding target genes (Marone et al., 2013).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==