Location: Food and Feed Safety Research
Project Number: 6054-42000-027-000-D
Project Type: In-House Appropriated
Start Date: Apr 19, 2021
End Date: Apr 18, 2026
1. Identify differentially expressed genes in resistant [R] and susceptible [S] corn lines that can serve as targets in controlling Aspergillus flavus and aflatoxin contamination. 2. Identify and characterize corn seed metabolites for enhancing resistance to aflatoxin contamination. 3. Develop and evaluate transgenic corn lines by over-expressing resistance-associated protein genes, or gene editing and silencing of Aspergillus flavus genes critical to growth and aflatoxin production. 4. Develop detection methods for pre- and post- harvest contamination in food supply and predictive modeling for mycotoxin contamination. Subobjective 4.A: Develop and evaluate different spectral-based imaging instruments for non-destructive detection of AF contamination in corn. Advance and commercialize hyperspectral-based, rapid and non-invasive, imaging technology. Subobjective 4.B: Develop methods to detect and prevent contamination of alternative proteins from plant-based sources with fungal toxins. Subobjective 4.C: Modeling mycotoxin contamination in the US and developing a forecast system to forewarn US stakeholders of mycotoxin contamination in corn.
Aflatoxin (AF) contamination in food and feed crops such as corn, peanut, cottonseed, and tree nuts, caused by Aspergillus flavus, is a global concern that compromises food safety and marketability. Aflatoxins are potent carcinogens and their contamination in food are one of the major causes of liver cancer worldwide. The most efficient and practical approach to reduce pre-harvest AF contamination in corn is the development of resistant lines, the overall goal of this project plan. We will delineate the molecular basis of A. flavus resistance in corn seeds through “systems biology” approach that will involve a combination of transcriptomic, proteomic, and metabolomic analyses. Identification of novel regulatory genes and gene networks that play key roles in host plant resistance against the fungus will contribute to the development of robust markers for use in marker-assisted breeding and/or to the identification of candidate genes for editing. We will apply functional genomics to identify key metabolic pathways (polyamines, carotenoids, flavonoids) that contribute to resistance against A. flavus and AF production. Transgenic corn expressing antifungal proteins/peptides that strongly inhibit A. flavus growth will be generated. In addition, “host induced gene silencing” approach will be used to target fungal genes that are critical for growth, pathogenesis, and production of AFs. In addition to food crops, safety of alternative proteins of plant origin from fungal toxins will also be appraised. Using artificial intelligence (AI) and machine learning (ML), algorithmic models will be constructed to predict in advance possible mycotoxin breakout so that timely mitigation efforts can be employed. Finally, non-destructive, hyperspectral-based imaging systems for several platforms will be refined and commercialized to detect AF contamination in stored kernels. The knowledge and products generated from this research will be invaluable for the consumers, stakeholder groups, scientific community, and regulatory agencies to protect and preserve food safety in the United States and abroad.