Large-Scale Data Mining Pipeline for Identifying Novel Soybean Genes Involved in Resistance Against a Terrible Yield Robber, SCN

Citation

Nour Nissan, Bahram Samanfar, Elroy Cober, Steve Molnar, James Green, Ashkan Golshani: Large-Scale Data Mining Pipeline for Identifying Novel Soybean Genes Involved in Resistance Against a Terrible Yield Robber, SCN. 20thAnnual OCIB Symposium, Ottawa Carleton Institute of Biology, 2023 Canada [Oral].

Résumé

Soybean (Glycine max (L.) Merr.) is one of the most economically valuable crops worldwide. Its seeds contain high oil and protein content making them essential in human food and animal feed. Soybean growth is being threatened by a plant parasitic nematode called soybean cyst nematode, Heterodera glycines Ichinohe, (SCN). Once SCN is present in the soil, eradication is nearly impossible. As of today, there are only two resistant genes being widely rotated in SCN infested fields, rhg1 and Rhg4. Hence, SCN populations are mutating and overcoming the resistance. Therefore, the identification of novel resistant genes in soybean is necessary. A multidisciplinary pipeline has been developed through utilizing functional genomics and systems biology practices; this involved training two leading sequence-based PPI prediction tools: Protein-protein Interaction Prediction Engine (PIPE4) and Scoring PRotein INTeractions (SPRINT) and using the bioinformatics tool REVIGO (reduce and visualize gene ontology terms). PIPE4 and SPRINT were used to search the entire soybean and SCN proteomes for proteins interacting with already known resistance proteins, rhg1 and Rhg4. A short-list of 956 candidate genes was attained from the starting list of 55,589 genes. Five of them contained gene ontologies (GO) related to response to nematode and 77 others contained other defense related GO terms. This pipeline enables researchers to focus their search on high-confidence targets to validate them as novel SCN resistance genes in soybean. The next step is to validate these genes through complementation analyses including an RNA-sequencing experiment and finally to develop allele specific markers for them.

Date de publication

2023-04-27