Location: Food and Feed Safety Research
2024 Annual Report
Objectives
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.
Approach
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.
Progress Report
The research objectives, which fall under National Program 108 Food Safety, Component 1, Foodborne Contaminants, are designed to understand the preharvest aflatoxin (AF, a toxic and carcinogenic compound) contamination of important food and feed crops, such as corn, caused by the fungus, Aspergillus (A.) flavus, and develop effective mitigation strategies.
To accomplish these objectives, it is important first to identify and evaluate native corn genes (Objective 1) and metabolites or chemicals (Objective 2) that contribute to resistance to aflatoxin contamination. Utilizing various molecular genetics approaches, several regions in maize chromosomes with roles in resistance against A. flavus infection and aflatoxin contamination have been identified previously. But such regions with low and inconsistent contribution to resistance create uncertainty for their use in marker-assisted breeding.
ARS researchers at New Orleans, Louisiana conducted a comprehensive meta-analysis (examination of data from a number of independent studies plus our own analysis) of all genomic regions reported thus far and identified 58 regions of interest distributed over all 10 maize chromosomes. Comparative analysis of the genes linked to these regions with published genes with roles in multiple stresses identified fourteen high-confidence genes that responded only to A. flavus infection and were not reported previously. Further evaluation of DNA markers linked to these genes suggested that three markers discriminated 14 AF resistant cultivars from eight susceptible varieties. These genes are extremely useful for marker-assisted breeding, but also, they are important candidates that can be targeted for gene editing (or genetic engineering) to develop AF resistant maize varieties.
Besides genes, there was no prior knowledge of involvement of small regulatory RNAs (known as microRNAs or miRNAs), which suppress the activity of target genes, in AF resistance of maize. From a study on time-scale expression profile of miRNAs in a resistant, moderately resistant, and susceptible varieties after A. flavus infection we identified, for the first time, 39 miRNAs that were found to express differently in these varieties. Further analysis using computational tools identified three important miRNA-target genes combinations with roles in AF resistance were identified. These newly identified candidate small RNAs and target genes along with the ones identified from the above meta-analysis will be used as targets for future gene editing work to develop maize varieties with AF resistance in collaboration with scientists at Louisiana State University, Baton Rouge, Louisiana (Agreement #6054-42000-027-004S).
In Objective 2, we continued to evaluate the roles of native corn metabolites or chemicals such as, flavonoids, provitamin A (or carotenoids) and polyamines (ubiquitous nitrogenous molecules), in reducing A. flavus growth and aflatoxin contamination. The mechanism of action by flavonoids on the fungus has been elucidated. We examined gene expression response of A. flavus after being treated with four different flavonoids and found many genes related to cell wall and cell membrane were upregulated. This supports our previous finding that flavonoids can modulate proliferation of the fungus as well as damage the cell wall. We have previously determined that the role of polyamines in plant disease resistance depends on the relative abundance of complex polyamines (such as, spermidine and spermine versus simpler diamines and breakdown products). Experiments designed to increase the levels of complex polyamines in corn by over-activation of the gene responsible for making the enzyme that produces the polyamine (S-adenosylmethionine decarboxylase or SAMDC) has been completed and the kernels overexpressing the polyamines will be analyzed or aflatoxin resistance. Evaluation of corn lines with different levels of carotenoids is also in progress.
Utilizing the results from Objectives 1 and 2 and from the sister project 6054-41420-009-00D, we continued our molecular breeding of corn for resistance to AF contamination under the Objective 3 including corn transformation (introducing foreign genes into corn) work that inserts a known antifungal gene(s) in corn. For example, genes for two antifungal synthetic peptide (small proteins), which demonstrated broad spectrum antimicrobial activity, were introduced into corn. This work is being performed in collaboration with Genvor LLC (Agreement #6054-42000-027-003C). We developed transgenic corn lines expressing one of the peptides (GV185) and these kernels will be evaluated for resistance to aflatoxin. We have also continued in generating transgenic RNAi corn lines capable of turning off the expression of several key A. flavus genes responsible for growth, infection, and toxin production using a technique called ribonucleic acid interference (RNAi). Recently in collaboration with scientists in Louisiana State University, Baton Rouge, Louisiana, a field test was conducted using RNAi corn lines capable of silencing an important fungal gene (called O-methyltransferase A or omtA or aflP) involved in AF production. Transgenic RNAi plants with silenced fungal omtA gene demonstrated significant reduction (72%) in aflatoxin contamination under field conditions. The resistance trait was transferred to elite corn lines by breeding and the progeny also showed a significant reduction aflatoxin contamination.
In addition to developing crops resistant to aflatoxin contamination, ARS researchers in New Orleans, Louisiana, in collaboration with the Department of Agricultural and Biological Engineering at Mississippi State University (MSU), Mississippi State, Mississippi, developed a non-invasive, inexpensive and rapid hyperspectral and multispectral imaging methodology that collects and processes information across the light-spectrum. This imaging technique aims to detect and quantify aflatoxins in corn kernels under Objective 4A. Collaborative studies with scientists at the Mississippi State University, Mississippi State, Mississippi on laboratory and field experiments with A. parasiticus mutants (Agreement # 6054-42000-027-006S) to determine the relationship between mutant products and imaging signatures failed to produce satisfactory samples for imaging. The laboratory-inoculated samples did not produce robust colonies and the field-inoculated samples showed cross contamination from co-occurring fungi. Currently, we are in the process of repeating both laboratory and field experiments, adjusting the protocols to maximize the potential of producing samples that will allow us to evaluate the effect of mutant strains.
Progress was made on a newly added activity under Objective 4B for evaluating the safety of protein-rich legumes (or pulses) which are becoming popular, reliable sources of alternative proteins. Initial experiments in this project involved lab inoculation experiments on chickpeas, lentils, and peas with A. flavus to assess their vulnerability to aflatoxin contamination. Results indicated that the legume seeds are resistant to aflatoxin contamination, in spite of limited fungal growth. Similar inoculation experiments on developing pods will be conducted with greenhouse-grown plants later.
Another new activity, Objective 4C, has also been added to develop predictive models that implement machine learning (ML) and artificial intelligence (AI). Region-specific mycotoxin contamination risks can be assessed using big data sets such as historical and forecasted weather sets, satellite collected parameters, and other sources of data. The overall objective is to develop computational predictive approaches that can be used to create a US-centric alert and predictive risk-assessment systems for stakeholders that provides a proactive window of opportunity to deploy mycotoxin mitigation strategies in advance. This objective will also include analysis of the impact of climate change on aflatoxin (AFL) and fumonisin (FUM) contamination in maize grown in the US in collaboration with scientists at Wageningen Food Safety Research in the Netherlands (Agreement #6054-41420-009-006S). Predictive models for Illinois and Iowa corn have already been published. Soil health has been determined as a key element in the probability of mycotoxin contamination at harvest time in corn. As such, we are currently developing models for Texas corn grown that are specific to different eco-regions to evaluate effects of various factors such as soil depth, calcium carbonate, water storage and availability and organic matter on mycotoxin contamination in collaboration with scientists at University of Texas at Arlington, Texas (Agreement # 6054-42000-027-007S).
Accomplishments
1. First demonstration of involvement of micro RNA or miRNA in aflatoxin resistance in maize. ARS researchers in New Orleans, Louisiana, in collaboration with researchers at Louisiana State University in Baton Rouge, Louisiana conducted a time-scale expression profile of miRNAs in the following corn varieties - a resistant TZAR102, moderately resistant MI82, and susceptible Va35 after A. flavus infection. For the first time, 39 miRNAs were found to express differently in the varieties. Further analysis using computational tools identified three important miRNA-target genes combinations with roles in AF resistance were identified. These newly identified candidate small RNAs and target genes identified previously from the comparative meta-analysis of resistant genes will be used as targets for future gene editing work to develop maize varieties with AF resistance.
Review Publications
Sweany, R.R., Cary, J.W., Jaynes, J.M., Rajasekaran, K. 2023. Broad-spectrum antimicrobial activity of synthetic peptides GV185 and GV187. Plant Disease. 107(10):3211-3221. https://doi.org/10.1094/PDIS-11-22-2572-RE.
Castano-Duque, L.M., Winzeler, H.E., Blackstock, J.M., Cheng, L., Vergopolan, N., Focker, M., Barnett, K., Owens, P.R., Van Der Fels-Klerx, I., Vaughan, M.M., Rajasekaran, K. 2023. Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning. Frontiers in Microbiology. 14. Article 1283127. https://doi.org/10.3389/fmicb.2023.1283127.
Branstad-Spates, E.H., Castano-Duque, L.M., Mosher, G.A., Hurburgh, Jr., C.R., Owens, P.R., Winzeler, H.E., Rajasekaran, K., Bowers, E.L. 2023. Gradient boosting machine learning model to predict aflatoxins in Iowa corn. Frontiers in Microbiology. 14. Article 1248772. https://doi.org/10.3389/fmicb.2023.1248772.
Gandham, P., Rajasekaran, K., Sickler, C., Mohan, H., Gilbert, M., Baisakh, N. 2024. Micro RNA (miRNA) profiling of maize genotypes with differential response to Aspergillus flavus implies zma-miR156–squamosa promoter binding protein and zma-miR398/zma-miR394-F-box cominations involved in resistance mechanism. Stress Biology. 4. Article 26. https://doi.org/10.1007/s44154-024-00158-w.