Finding patterns in the genes and conditions that make microbes resilient will help develop strategies to reduce foodborne illness
Image Credit: Michael Ocampo, creative commons via Wikimedia Commons
Every year, about one in six Americans gets sick from contaminated food. The germs that cause some of the most troubling foodborne illnesses, like E. coli, Salmonella, and Listeria pop up occasionally throughout the food supply system, but outbreaks are hard to predict, because scientists don’t yet know enough about how the germs survive and proliferate.
Research by Professor Abani Pradhan in the Department of Nutrition and Food Science and the Center for Food Safety and Security Systems at the University of Maryland will fill in some of the missing information. The U.S. Department of Agriculture’s National Institute of Food and Agriculture has awarded Pradhan $591,000 to develop new tools using genomics and machine learning to better predict the conditions that can lead to foodborne illness outbreaks.
“The whole genome sequencing and metagenomics data are mostly available or can be generated on all these pathogens that cause these very serious food borne illnesses,” said Pradhan, “but there is really not a simple tool for using it to improve food safety. We are developing models that could be really helpful in understanding pathogen persistence and improving surveillance.”
Pradhan and his team will use machine learning to analyze the genomes of Salmonella, Listeria and E.coli from publicly available databases to find genetic indicators—like specific genes, mutations, or higher or lower levels of gene expression—that help them persist in the environment. These indicators could help the pathogens resist cleaning agents, evade human immune responses or survive in certain temperature and moisture conditions.
Then the team will evaluate the environmental conditions surrounding outbreaks of these diseases from poultry and leafy green facilities. They will look at things like temperature, moisture, and the microbiome in food processing environments, such as the soil where leafy greens grow, or the water used for irrigation.
By combining all of this information with the genomic data and analyzing it with machine learning, the team expects to find patterns that can more accurately predict what conditions and circumstances help various strains of foodborne pathogens survive and proliferate.
Their final step will be to test their methods by comparing the predictions of their machine learning model in real-world settings. Whether in a laboratory greenhouse or farm environment, Pradhan will partner with collaborators from the USDA and the U.S. Food and Drug Administration (FDA) to evaluate the work.
Pradhan’s ultimate goal is to develop a digital “dashboard” that can help decision-makers predict and track the potential persistence or emergence of pathogens like E. coli, Salmonella and Listeria and take appropriate actions to prevent outbreaks.
