Informed Design

The goal of our project is to build a sensor to quickly detect enterohaemorrhagic E. coli (EHEC) to prevent foodborne illness caused by E. coli contaminated beef. This project was inspired by recent recalls of infected beef in Alberta and abroad (maybe cite). When we started on the project, we learned that the industry searches for E. coli in meat products after slaughter. We brainstormed methods in which we could supercede accepted methods such as PCR and bacterial culturing, but as we dived deeper into the problem, our lack of understanding of how cattle get turned into beef became apparent. We needed to learn the intricacies of the beef business. And so, we sought out the people who work with cattle day to day.

The Rancher

To start this dialogue, we spoke to Dr. Bob Church, a prominent Alberta rancher, researcher, and expert who has forwarded genetic technologies into the livestock business. We inquired about how the cattle business operates and proposed some ideas to detect E. coli in consumer meat. He answered our questions and spoke about the tremendous sensitivity for testing E. coli in beef. To remain competitive with current industry methods, we would have to sense five individual bacteria in about a pound of beef!

After this reality check, Dr. Church proposed a different approach which shifted the entire focus of our project. He told us to consider developing tools to monitor E. coli in cattle prior to slaughter. We learned in populations of beef cattle, about five percent of the animals are “super shedders” because they pass three to four higher orders of magnitude of dangerous E. coli in their feces.

Could food safety could be improved by separating “super shedders” before they contaminate processing plants? We decided to build a device to rapidly test cattle feces to determine an animal’s shedding level. Our intuition told us that by bringing less E. coli into processing plants, there would be fewer opportunities for food contamination. We wondered whether slaughtering plants would agree.

The Meat Processor

We proposed this idea to Ryan Clisdell, the technical service manager from Cargill Meat Solutions in High River, Alberta, who oversees the processing of 4500 cattle daily. He took our team into the slaughter plant and explained how his team prevents E. coli contamination through safe butchering practices. The mechanization and choreography of the butchering process to lessen opportunities for contamination was remarkable. However, this left us wondering what happens when employees miscommunicate or fail to follow proper company protocols?

Our tour of the Cargill plant settled us on developing a system to monitor E. coli shedding in cattle prior to slaughter. By developing such a system, we hoped to introduce new tools so that E. coli could be managed at additional points during production. This idea would complement current E. coli management programs used by companies such as Cargill and provide another layer of food safety.

We needed to know where a pre-slaughter E. coli management program could be implemented in industry. Cargill gave us positive feedback and told us that super shedders could be screened and processed differently based on E. coli. But we were curious if performing these tests earlier in the supply chain might cause a greater effect on food safety. Would feedlots consider adopting this idea and use the data to isolate super shedders?

The Feedlot

We took these questions to Dr. Eric Behlke, a veterinarian from Feedlot Management Services Ltd., who took us on a tour of Chinook Feeders, a large scale Alberta feedlot holding (18 000??) head of cattle. Dr. Behlke received our idea positively, but he challenged us to build a system which could be integrated into existing feedlot infrastructure. We saw opportunities where cattle could theoretically be monitored on a single animal basis, but only at specific periods when they are processed for vaccines and checkups. During the tour, Dr. Behlke also showed how the feedlot has computerized systems to collect and assign data to individual animals.

We concluded that combining these routine checkups with the animal information database, feedlots could monitor shedding in individual animals. However, their feedback revealed three design considerations which we’d have to incorporate into our prototype. Firstly, it would have to be cheap so that it could be scaled-up to entire feedlots. Second, it would have to be easy to use by non-laboratory employees in feedlots. And finally, it would have to provide a definitive measure of shedding levels in under an hour during routine check-up procedures.

From these three discussions, we incorporated feedback from critical beef stakeholders covering the complete beef cattle lifecycle. To ensure ease of use, we settled on constructing a prototype resembling a home pregnancy test, to give an easy to read visual yes or no answer as to whether an animal is a super shedder. Our team decided to use an in vitro based sensor, using components extracted from cells, to make the system more portable and reliable in the field. Our selection of common paper materials for the prototype was also justified based on price.

We believed that we were on the right track to developing something of relevance to the beef cattle industry. But we still questioned whether it would be wise to monitor E. coli in individual animals. It was apparent that because of the density of cattle in feedlots, super shedding from a few animals in the pen would expose all neighbouring animals to higher levels of this pathogen.

To answer these questions, we engaged a company called Bioniche, a manufacturer of a bovine E. coli O157 vaccine. We spoke to Rick Culbert, the President, and Susan Goebel, the E. coli project manager, about where they saw our ideas fitting into the industry. These individuals have worked tirelessly to bring their vaccine to market and are experts in management of E. coli prior to slaughter.

This meeting was critical because they revealed the broader implications which our E. coli monitoring tool could have in the livestock industry. We learned that approximately one-third of E. coli O157 illness is due to contaminated beef and another third from contaminated produce. The final third of human infections stem from E. coli in the broader environment, from sources such as contamination of water and exposure of children who touch livestock carrying the bacteria. We learned that though there are livestock management practices, including vaccines and dietary changes, which can be used to reduce E. coli shedding from beef cattle, there is little incentive for the beef cattle industry to adopt such practices because such bacteria are not detrimental of the well-being of the animals. We realized that our tool could be used to provide a visual indicator of E. coli shedding. With such feedback, our tool could be leveraged with other management strategies to decrease E. coli levels across cattle populations.