Team:Calgary/Project/HumanPractices/InformedDesign
From 2013.igem.org
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<p>After this reality check, Dr. Church proposed a different approach that shifted the entire focus of our project. He told us to consider developing tools to monitor <i>E. coli</i> in cattle <span class="Yellow"><b>prior to harvest.</b></span> We learned in populations of beef cattle, about five percent of the animals are <span class="Yellow"><b>“super shedders”</span></b> because they pass three to four higher orders of magnitude of dangerous <i>E. coli</i> in their feces.</p> | <p>After this reality check, Dr. Church proposed a different approach that shifted the entire focus of our project. He told us to consider developing tools to monitor <i>E. coli</i> in cattle <span class="Yellow"><b>prior to harvest.</b></span> We learned in populations of beef cattle, about five percent of the animals are <span class="Yellow"><b>“super shedders”</span></b> because they pass three to four higher orders of magnitude of dangerous <i>E. coli</i> in their feces.</p> | ||
- | <p>Could food safety 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 <i>E. coli</i> into processing plants would lead to fewer opportunities for contamination. We wondered | + | <p>Could food safety 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 <i>E. coli</i> into processing plants would lead to fewer opportunities for contamination. We wondered whether the processing plants would agree.</p> |
<h2>The Meat Processor</h2> | <h2>The Meat Processor</h2> | ||
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<p>We concluded that by combining these routine checkups with the animal information database, feedlots could monitor super shedders in individual animals. However, their feedback revealed three <span class="Yellow"><b>design considerations </b></span>which we would have to incorporate into our prototype. First, it would have to be <span class="Yellow"><b>cheap</span></b> so that it could be scaled-up to entire feedlots. Second, it would have to be <span class="Yellow"><b>easy to use</span></b> by non-laboratory employees in feedlots. And third, it would have to provide a <span class="Yellow"><b>definitive measure</span></b> of EHEC shedding levels within an hour to be used during routine check-up procedures.</p> | <p>We concluded that by combining these routine checkups with the animal information database, feedlots could monitor super shedders in individual animals. However, their feedback revealed three <span class="Yellow"><b>design considerations </b></span>which we would have to incorporate into our prototype. First, it would have to be <span class="Yellow"><b>cheap</span></b> so that it could be scaled-up to entire feedlots. Second, it would have to be <span class="Yellow"><b>easy to use</span></b> by non-laboratory employees in feedlots. And third, it would have to provide a <span class="Yellow"><b>definitive measure</span></b> of EHEC shedding levels within an hour to be used during routine check-up procedures.</p> | ||
- | <p>From these three discussions, we incorporated feedback from critical beef <span class="Yellow"><b>stakeholders</span></b> covering the complete beef cattle lifecycle. To ensure ease of use, we settled on constructing a prototype resembling a <span class="Yellow"><b>home pregnancy test</span></b>, to give an easy to read <span class="Yellow"><b>visual yes or no</span></b> answer as to whether an animal was a super shedder. We decided to make a <span class="Yellow"><b>DNA sensor | + | <p>From these three discussions, we incorporated feedback from critical beef <span class="Yellow"><b>stakeholders</span></b> covering the complete beef cattle lifecycle. To ensure ease of use, we settled on constructing a prototype resembling a <span class="Yellow"><b>home pregnancy test</span></b>, to give an easy to read <span class="Yellow"><b>visual yes or no</span></b> answer as to whether an animal was a super shedder. We decided to make a <span class="Yellow"><b>DNA sensor</span></b>. A DNA sensor is more reliable than a protein sensor, as proteins can get degraded during sample preparation. In addition, targeting proteins requires antibodies, which dramatically increase the cost of our system. Our team decided to use an <span class="Yellow"><b>in-vitro based sensor</span></b>, 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 influenced on the low cost to build.</p> |
<h2>Other "Steak" holders</h2> | <h2>Other "Steak" holders</h2> | ||
- | <img align="right" style="width: | + | <img align="right" style="width:25%;" src="https://static.igem.org/mediawiki/2013/2/20/Calgary_IAMASTEAK.png" style="float: left; clear: both;"></img> |
<p>We believed that we were on the right track to developing something of <span class="Yellow"><b>relevance to the beef cattle industry</span></b>. But we still questioned whether it would be wise to monitor <i>E. coli</i> 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.</p> | <p>We believed that we were on the right track to developing something of <span class="Yellow"><b>relevance to the beef cattle industry</span></b>. But we still questioned whether it would be wise to monitor <i>E. coli</i> 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.</p> |
Latest revision as of 03:57, 29 October 2013
Informed Design
Informed Design
The goal of our project is to build a sensor for rapid detection of enterohemorrhagic E. coli (EHEC) to prevent foodborne illness caused by these pathogens in water, vegetables and beef. This project was inspired by recent recalls of infected beef in Alberta and outbreaks in North America and Europe where water supplies and produce can become contaminated with these pathogens. At the early stages of planning we realized that the industry works reactively to search for E. coli in meat products after harvest. Initially, we brainstormed methods where we could supersede 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 the food on plates became apparent. We needed to learn how the beef industry works. To inform our design, we sought out the people who work with cattle every day.
The Rancher
To start this dialogue, we spoke to Dr. Bob Church, a prominent Alberta rancher, researcher, and expert, who has helped bring genetic technologies into the livestock business. We asked him how cattle businesses operate and proposed some ideas to detect E. coli in beef. 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 that shifted the entire focus of our project. He told us to consider developing tools to monitor E. coli in cattle prior to harvest. 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 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 would lead to fewer opportunities for contamination. We wondered whether the processing plants would agree.
The Meat Processor
We proposed this idea to Ryan Clisdell, the Food Safety, Quality & Regulatory manager from Cargill Meat Solutions in High River, Alberta, who oversees the processing of 4,500 cattle daily. He took our team into the harvest facility and explained how his team helps prevent E. coli contamination through food safe harvest practices. The mechanization and choreography of the harvest process to lessen opportunities for contamination was remarkable. However, this left us wondering whether addressing the root cause of E. coli contamination, by helping prevent or limiting how much is present on live animals, would further help to prevent the chance of food safety illness?
Our tour of the Cargill plant settled us on developing a system to monitor E. coli shedding in cattle prior to harvest. 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-harvest 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 cattle 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 which manages 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 by combining these routine checkups with the animal information database, feedlots could monitor super shedders in individual animals. However, their feedback revealed three design considerations which we would have to incorporate into our prototype. First, 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 third, it would have to provide a definitive measure of EHEC shedding levels within an hour to be used 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 was a super shedder. We decided to make a DNA sensor. A DNA sensor is more reliable than a protein sensor, as proteins can get degraded during sample preparation. In addition, targeting proteins requires antibodies, which dramatically increase the cost of our system. 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 influenced on the low cost to build.
Other "Steak" holders
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 realized that our tool could be used to provide a visual indicator of E. coli shedding, and could have far-reaching implications. With such feedback, our tool could be leveraged with other management strategies to decrease E. coli levels across cattle populations.
From our dialogue with key players at in stage of the beef-processing industry, we were able to not only gain validation that our project was needed and worthwhile, but also able to implement multiple design considerations into our final product. We hope that this will result in the design and construction of a meaningful and relevant system where we can use synthetic biology to solve a very serious problem worldwide.