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Ontario Grain Farmer Magazine

A computational/machine learning approach to identify novel white mold resistance genes in soybean

Principal Investigator: Bahram Samanfar

Research Institution: Agriculture and Agri-Food Canada

Timeline: June 2024 – March 2027  

Objectives:

  • High-throughput proteomics (protein-protein interaction) network of soybean and white mold host pathogen interactions.
  • High-throughput transcriptome-wide network of micro RNAs (miRNAs) targeting soybean and/or white mold.

Impacts:

  • Higher Yields: Discovering novel resistance genes and target proteins through this research can lead to the development of white mold-resistant soybean varieties. This, in turn, can result in higher soybean yields for Ontario’s grain farmers.
  • Reduced Production Costs: Resistance genes identified in the study may reduce the reliance on costly fungicides, leading to cost savings in soybean cultivation.
  • Enhanced Disease Management: Understanding microRNA-mRNA interactions can lead to the development of precise interventions for white mold management. This can result in reduced disease impact and increased soybean yields.
  • Enhanced Crop Improvement: The development of allele-specific markers will facilitate the breeding of white mold-resistant soybean varieties. Farmers adopting these varieties can expect more reliable crop performance and potentially higher profits.
  • Economic Growth: By improving soybean yields and reducing production costs, this research can contribute to the economic growth of Ontario’s grain farming sector, creating new opportunities and bolstering the sustainability of soybean cultivation.

Scientific Summary:

White mold, also known as Sclerotinia stem rot, is a fungal disease that poses a significant problem for soybean crops in Ontario. The fungus Sclerotinia sclerotiorum thrives in cool, moist conditions and infects soybean plants through their dying flowers. It can cause wilting, browning, and premature death of branches, stems, and pods, leading to significant yield losses. Managing white mold involves crop rotation, planting partially resistant varieties, and fungicide application. As the threat of white mold persists within Ontario’s agricultural sector, soybean cultivators in this region maintain a vigilant stance, continually enhancing their strategies and embracing innovative measures. One potential initiative involves the adoption of new Ontario-adapted soybean lines featuring novel sources of resistance. Soybean yield loss to white mold was an estimated 1.2 million bushels in 2021. There is currently a need to identify additional, novel white mold resistance sources.

Although previous studies have mapped resistance QTLs, the identity of most of the underlying resistance genes remain unknown. This project represents an opportunity to understand the molecular biology of white mold and lead to the improvement of the control of white mold. During our past Grain Farmers of Ontario (GFO) and Canadian Field Crop Research Alliance (CFCRA) projects, we developed computational pipelines to predict protein-protein interactions (PPI) and micro RNAs (miRNAs) in soybean cyst nematode (SCN) and soybean but not in white mold. We plan to predict PPI and miRNA-mRNA interactions in the white mold-soybean cross intersection. Furthermore, we aim to identify candidate genes related to soybean and white mold involved in either protein-protein interactions or miRNA-mRNA interactions predicted through bio-informatic tools. Genes identified in host-pathogen interactions present a promising opportunity to explore the possibility of suppressing their activity using a miRNA-based approach. Findings from this proposal may allow development of allele-specific markers, to generate Ontario adapted white mold resistant varieties.

External Funding Partners:

This project was funded in part by the Government of Canada under the Sustainable Canadian Agricultural Partnership’s AgriScience Program.