Big DNA Datasets Helping Predict Crop Defense in Fight with Fungus Trent University
For the past ten years, Trent University molecular biologist and forensic science educator Dr. Barry Saville has been studying the attack strategy of the wheat leaf rust fungus on Canadian wheat.
The innovative Precision Agriculture to Improve Crop Health Project, led by Terramera, is a partnership between Trent University, Sightline, Compression AI, BC Cancer Research, Simon Fraser University, Agriculture and Agri-Food Canada, University of Saskatchewan, Genome BC, and Canada’s Michael Smith Genome Sciences Centre. Its goal is to tackle the challenges facing the increased global demand for food resources while seeking to protect crops and reduce pesticide use.
Working alongside Agriculture and Agri-Food Canada research scientist Dr. Guus Bakkeren, Professor Saville has used large-scale next-generation sequencing approaches to uncover genetic indicators for how wheat leaf rust fungal isolates are able to infect wheat by overcoming its defense mechanisms.
Now, this data will be used to develop a new biotechnology platform that helps control pests and pathogens that can cost Canadian farmers more than $100 million annually.
Prof. Saville and Trent University will collaborate specifically on expanding the genomics-type data. “It is really exciting to take the next step in our long-term study and contribute our results to agricultural innovations with the potential for large scale economic impact,” said Prof. Saville. “My work with Dr. Bakkeren was focused on gathering genomic and transcriptomic data of various leaf rust fungal isolates that infect wheat plants with known resistance to the fungus. We were collecting genetic clues as to how this fungus can trick the wheat plants so that they sustain the life of the fungus instead of fighting it off. Now, this data will be used to design technologies and screen for new compounds that prevent disease development in wheat.”
The extensive database on wheat leaf rust transcriptomes developed by Prof. Saville with Dr. Bakkeren and others will be augmented and used by partners on the Precision Agriculture project who have expertise in software development and machine learning. A goal of the investigations will be to use machine learning to predict potential infection outcomes given the detection of specific patterns of gene expression. The data will be used to help train the software to recognize patterns.
Prof. Saville will also continue to work with Dr. Bakkeren testing the susceptibility of wheat crops to infection by wheat leaf rust to gather more data for the partners to use in their analyses.
Trent University’s partnership with the Digital Technology Supercluster, recently announced by Canada’s Minister of Innovation, Science and Industry Navdeep Bains, will see the University as a key part of the industry-led consortium under the umbrella of Canada’s Innovation Superclusters Initiative worth $950 million, aiming to position Canada as a global leader in digital technology innovations.
Posted on August 28, 2020