Soybean cyst nematode (SCN): overcoming the tiny beast below the surface


Nour Nissan, Elroy Cober, Ashkan Golshani, Bahram Samanfar: Soybean cyst nematode (SCN): overcoming the tiny beast below the surface. Ontario Soybean and Canola Committee (OSACC), 2021, Canada [Oral].


Cultivated soybean (Glycine max (L.) Merr.) has become an economically important crop in Canada and is regularly used for protein and oil in human consumption, animal feed, industrial products and as an important element in sustainable agricultural management practices due to its nitrogen fixation capability.
Soybean cyst nematode, Heterodera glycines Ichinohe, (SCN) is a plant parasitic nematode which is becoming an overwhelming pest of soybean on a global scale. Symptoms of compromised soybean may include chlorosis of the leaves and stems, root necrosis and stunting which will result in significant loss in yield, varying between 5% to 80%, and making infected plants susceptible to other diseases. SCN is an incredibly challenging nematode that once present in the field becomes almost impossible to eradicate. Some of the solutions that allow us to overcome this beast are: resistant lines, crop rotations and field management. SCN’s sequenced genome is comprised of about 20,000 coding regions, however its biology is not well understood. As of today, only two resistant genes have been identified, rhg1 and Rhg4; as one can imagine, the two SCN resistant genes are not enough to take on the frightening nematode underground for long, as there is a strong possibility for resistance breakdown and more aggressive SCN populations in the future. Having said that, in order to have sustainable farming with high soybean yield, novel resistant genes against SCN in soybean are necessary.
The primary goal of this project is to use a functional genomics approach to identify novel candidate genes involved in soybean-SCN host-pathogen interactions to facilitate development of SCN resistant cultivars. To this end, we have used a bioinformatics tool called PIPE (Protein-protein Interaction Prediction Engine) integrated with previous RNA-seq and GWAS studies. A short list of (~50) potential candidate genes have been selected for further investigations through complementary bioinformatics analysis and molecular biology related practices. This approach will potentially lead into identification of novel resistance genes to SCN which will be used in breeding programs to generate SCN resistant soybean lines.

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