Bioinformatics and Molecular Biology Approach to Identifying Novel Sources of Resistance Against a Terrible Yield Robber, SCN.
Nour Nissan, Elroy Cober, Jim Green, Ashkan Golshani, Bahram Samanfar: Bioinformatics and Molecular Biology Approach to Identifying Novel Sources of Resistance Against a Terrible Yield Robber, SCN. Soy2022
Soybean (Glycine max (L.) Merr.) is an important partner in sustainable agricultural management practices, as well as an important source of food, feed, and fuel. Soybean growth is being threatened by a plant parasitic nematode called soybean cyst nematode, Heterodera glycines Ichinohe, (SCN) causing yield losses of up to 80%. Once the pest is present in the soil, irradiation is nearly impossible. As of today, there are only two resistant genes being widely used/rotated in the field, rhg1 and Rhg4. Due to this, resistance is starting to break-down as SCN populations are mutating and learning how to reproduce on the current resistant varieties. Hence, the identification of novel resistant genes against SCN in soybean are necessary to save this crop. A multidisciplinary pipeline has been developed through utilizing functional genomics and systems biology practices. This involved investigating various computational databases, including single nucleotide polymorphisms (SNP), loss of function (LOF), and gene ontology (GO), in addition to developing two computational protein-protein interaction prediction engines for soybean and SCN: protein-protein interaction prediction engine (PIPE) and scoring protein interactions (SPRINT). These two computational engines were used to search the entire soybean and SCN genomes (~60,000 genes and ~30,000) for genes interacting with already known resistance genes, Rhg1 and Rhg4 resistance genes in a process called guilt by association. As well, the various other databases mentioned were also used to help develop the short list of candidates potentially involved in resistance against this deadly yield robber, SCN. Through the use of this pipeline on the genome-wide approach, a short-list of 10 candidates was attained. Further investigation of the 10 potential candidate genes is currently in progress through RNA-seq experiments, and qPCR for further validation. Finally, allele-specific marker development will cap this project to assist breeders in quickly scanning hundreds of varieties for this novel resistance to use in the field.