Bioinformatic-Based Approaches for Disease-Resistance Gene Discovery in Plants

• Published in: Agronomy (Basel) Vol. 11; no. 11; p. 2259
• Main Authors: Fernandez-Gutierrez, Andrea, Gutierrez-Gonzalez, Juan J.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2021
• Subjects: Annotations; Barley; Binding sites; Chromosomes; Cloning; Crop diseases; Crop yield; Crops; Disease resistance; Genes; genome complexity reduction; Genomes; Host plants; Immune system; Kinases; Leucine; NBS-LRR genes; NLRs; Nucleotides; pathogen resistance; Pathogens; Pesticide use reduction; Pesticides; Pipelines; Plant growth; Plant resistance; Production increases; Proteins; R genes; Receptors; Wheat
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy11112259

Pathogens are among the most limiting factors for crop success and expansion. Thus, finding the underlying genetic cause of pathogen resistance is the main goal for plant geneticists. The activation of a plant’s immune system is mediated by the presence of specific receptors known as disease-resistance genes (R genes). Typical R genes encode functional immune receptors with nucleotide-binding sites (NBS) and leucine-rich repeat (LRR) domains, making the NBS-LRRs the largest family of plant resistance genes. Establishing host resistance is crucial for plant growth and crop yield but also for reducing pesticide use. In this regard, pyramiding R genes is thought to be the most ecologically friendly way to enhance the durability of resistance. To accomplish this, researchers must first identify the related genes, or linked markers, within the genomes. However, the duplicated nature, with the presence of frequent paralogues, and clustered characteristic of NLRs make them difficult to predict with the classic automatic gene annotation pipelines. In the last several years, efforts have been made to develop new methods leading to a proliferation of reports on cloned genes. Herein, we review the bioinformatic tools to assist the discovery of R genes in plants, focusing on well-established pipelines with an important computer-based component.