A Bioinformatics In-Silico Approach to Obtaining a Short-List of Candidates for Novel Sources of Resistance Against a Terrible Yield Robber, SCN

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). As of today, there are only two resistant genes being widely rotated in SCN infested fields, rhg1 and Rhg4. Due to this, SCN populations are mutating and overcoming the resistance. Hence, the identification of novel resistant genes in soybean is 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, loss of function, TAIR10 and BLASTP, in addition to two leading sequence-based PPI prediction tools: Protein-protein Interaction Prediction Engine (PIPE4), and Scoring PRotein INTeractions (SPRINT). These two computational engines were used to search the entire soybean (~60,000 proteins) and SCN proteomes (~30,000 proteins) for proteins interacting with already known resistance proteins, rhg1 and Rhg4. Using this pipeline on entire soybean genome, a short-list of 56 candidate genes was attained. Of these candidates, 4 were published about recently, one SCN resistance, and 3 in resistance to other pathogens. Of the remainder, 16 genes contained annotations related to pathogen resistance in plants. Further investigation of these potential candidate genes is currently in progress through an RNA-seq experiment. Finally, allele-specific marker development will cap this project to assist breeders in quickly scanning for this novel sources of resistance.