Molecular Plant Breeding 2024, Vol.15, No.3, 144-154 http://genbreedpublisher.com/index.php/mpb 147 4 Identification and Characterization of Disease Resistance Genes 4.1 Techniques for gene discovery The discovery of disease resistance genes (Rgenes) in plants has evolved significantly over the past few decades. Traditional biochemical methods, such as cloning and mutagenesis, have been foundational in identifying these genes. However, with the advent of next-generation sequencing (NGS) and bioinformatics, the landscape of gene discovery has expanded dramatically. NGS allows for the rapid sequencing of entire genomes, facilitating the identification of Rgenes through comparative genomics and transcriptomics (Hadjadj et al., 2019). Bioinformatic tools have become indispensable, enabling researchers to predict R genes by analyzing sequence data for characteristic domains such as nucleotide-binding sites (NBS) and leucine-rich repeats (LRR) (Figure 2) (Fernandez-Gutierrez and Gutierrez-Gonzalez, 2021). These methods are complemented by functional genomics approaches, which involve the use of transposon mutagenesis and metagenomics to uncover new resistance mechanisms (Hadjadj et al., 2019). Figure 2 Overview of protocols for NLR discovering pipelines (Adopted from Fernandez-Gutierrez and Gutierrez-Gonzalez, 2021) Image caption: Equal and differential steps are lined up to highlight the similarities/differences. WT: wild type. NGS: next generation sequencing. SNV: single nucleotide variant. A figure legend is on the upper right corner (Adopted from Fernandez-Gutierrez and Gutierrez-Gonzalez, 2021) 4.2 Functional genomics approaches Functional genomics plays a crucial role in understanding the mechanisms by which R genes confer disease resistance. This field employs various techniques to elucidate gene function, including gene knockout and overexpression studies, RNA interference (RNAi), and CRISPR-Cas9 mediated gene editing. These approaches allow researchers to observe the phenotypic effects of specific gene modifications, thereby linking gene sequences to their functional roles in disease resistance (Hadjadj et al., 2019). Additionally, functional genomics can involve the use of transcriptomic and proteomic analyses to study gene expression patterns and protein interactions under pathogen attack, providing insights into the dynamic responses of plants to infections (Fernandez-Gutierrez and Gutierrez-Gonzalez, 2021).
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