Team:Lethbridge HS/Description
When looking for inspiration for this year’s project, we wanted to tackle a relevant issue that would impact our future. Many of our team members are interested in the medical field; therefore, we decided to focus on improving current medical practices. We came across the emergence of antibiotic-resistant bacteria. It is a rapidly growing problem in the world, and we were motivated to tackle it out of concern for the future.
The combination of wait times, 1-3 day wait time for current diagnostic results (Boston Children’s Hospital) and the typical use of broad spectrum antibiotics (Claridge et al., 2010) has increased the health risk of infection and the spread of antibiotic resistant bacteria. On average, a third of prescribed antibiotics are deemed unnecessary when treating sick patients (CDC 2016). Furthermore, misdiagnosis with these broad spectrum antibiotics are found to be less or non-effective to the infection, placing patients at a higher risk; especially since with the rise of antibiotic resistant strains, there is also a rise in patient mortalities (OECD 2018).
Table 1: Analysis of current diagnostic methods and wait times
| Methods | Accuracy | Time | Cost |
|---|---|---|---|
| Bacterial Culturing | 
| 16 Hours | $0.26-$62.00* |
| Symptom diagnosis |  |
Within appointment | $100-$150**(U.S resident) |
* Lu et al., 2013
**https://www.debt.org/medical/emergency-room-urgent-care-costs/
It is apparent both bacterial culturing and symptom diagnosis are costly, whether it being costly in time, potentially putting risk to the patient in that timeframe, or potentially costly in financial terms. To improve the quality of life for patients there is a need to combine the quick timeframe with symptom diagnosis and the cost of bacterial culturing while ensuring that misdiagnosis is minimized.
With these factors in mind, we decided to build a faster and more efficient diagnostic system that could identify pathogens and provide accurate antibiotic treatments, thus decreasing the misdiagnosis and spread of antibiotic resistant bacterial strains. Thus, C.A.D.A.R was created.
We found several previous iGEM teams that utilized CRISPR technologies for various purposes, including detection, such as Munich’s 2017 team and the TU Delft 2017 team. These teams inspired us to further research the CRISPR Cas13a system, and we discovered that it would be an effective solution to the issue of antibiotic resistance. It would allow us to create a system to more accurately detect and diagnose bacterial infections, thus allowing us to cut down on the usage of broad-range antibiotics and decrease the potential of creating antibiotic-resistant bacteria. Additionally, we have developed our system with modularity in mind, meaning that it can be used to detect a variety of bacteria.
Our project would involve both a system for the detection and one for the treatment of disease. The detection system would consist of a paper strip upon which CRISPR-CAS13a and RNA mango, an RNA aptamer that is able to fluoresce, are expressed.
After taking a sample from a patient and placing it on the strip, the detection system would indicate if a specific bacteria were present by Cas13a cleaving targeted bacterial RNA and the collateral RNA mango, causing a decrease in colour. This indicates that the bacteria is present and the appropriate antibiotic for that bacteria can be prescribed. The detection system can be used not only in a medical setting, but also to detect bacteria found on food products. Additionally, an accessible and effective method of detecting tick-borne diseases would be a very useful application of this system, especially here in southern Alberta where those diseases are an issue.
We also realized that CRISPR Cas13a could be used as a therapeutic to specifically target pathogenic bacteria that would disturb the microbiome.
Using a phagemid system containing CRISPR Cas13a, we would be able to detect a specific sequence of bacterial RNA. This provides an alternative bacteria that would prevent increasing the number of antibiotic resistant bacteria.
References
CDC. 2016. CDC: 1 in 3 antibiotic prescriptions unnecessary [Internet]. Retrieved 2019 from: https://bit.ly/2mwqqcj
Claridge JA, Pang P, Leukhardt WH, Golob JF, Carter JW, Fadlalla AM. Critical Analysis of Empiric Antibiotic Utilization: Establishing Benchmarks. 2010;(2):125–131.
Lu C, Liu Q, Sarma A, Fitzpatrick C, Falzon D, et al. (2013) A Systematic Review of Reported Cost for Smear and Culture Tests during Multidrug-Resistant Tuberculosis Treatment. PLOS ONE 8(2): e56074. https://doi.org/10.1371/journal.pone.0056074
OECD. 2018. Stemming the Superbug Tide: Just A Few Dollars More, OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/9789264307599-en.