Can Genetics Predict Antibiotic Resistance to Reduce Unnecessary Antibiotic Use?

Project Title

Rapidly and Accurately Identifying Antimicrobial Resistant Mycoplasma Bovis in Feedlot Cattle


Dr. Matthew Links (University of Saskatchewan)

Dr. Murray Jelinski (Western College of Veterinary Medicine)

Status Project Code
Completed January, 2021 POC.01.19


With changing regulatory and market requirements, the beef industry is facing increasing scrutiny regarding antimicrobial use for prevention, metaphylaxis, and treatment.Antimicrobials are critical to ensure the health, welfare and productivity of cattle exposed to bovine respiratory disease. However, current laboratory diagnostic support for prophylaxis, metaphylaxis, to inform appropriate and timely treatment is inadequate. M. bovis is just one of the bacteria found in cattle diagnosed with BRD, but it can also cause other serious impacts, including Chronic Pneumonia and Polyarthritis Syndrome (CPPS). CPPS is not only economically significant, but also results in significant welfare issues due to extreme lameness. There is no effectivevaccine against M. bovis despite exhaustive efforts, making early detection and treatment even more crucial for cattle management. Although it is currently possible to test for antibiotic resistance in M. bovis, the current method of culturing takes days to weeks, is costly, and requires specialized sampling materials.


  • Identify the set of biomarkers that can track antimicrobial resistance in Mycoplasma bovis using a genetic algorithm.

What theY DID

This project was built on previous work developing a genetic algorithm that can identify the key locations within a genome to screen for antimicrobial resistance. Applying this computational method to samples collected from sick and healthy cattle from the past 10 years the researchers were able to identify the DNA sequences that correspond to M. bovis which are highly resistant vs. highly susceptible to antibiotics.

This work will be essential in advancing the beef feedlot production diagnostic support towards the realm of “pen-level precision medicine” – to make informed treatment decisions for each individual animal production group. The DNA sequences identified in this work provide the targets for diagnostic tests which could be deployed chute-side and used to assess animals entering the feedlot. Additionally, these DNA sequences provide targets for future research into how M. bovis adapts to antibiotic use. These improvements are critically needed to maintain consumer confidence in beef quality and safety, address antimicrobial stewardship requirements, meet end-user needs and maintain global market access.

What You Learned

For each of 9 different antimicrobials (enrofloxacin, tildipirosin, gamithromycin, tulathromycin, florfenicol, oxytetracycline, chlortetracycline, tilmicosin and tylosin tartrate) a set of 10 SNPs were identified that can characterize M. bovis as highly susceptible or highly resistant to the drug. The way in which the SNPs are identified is in a type of genetic simulation where the computer evolves all sets of 10 SNPs modeling the mixing of genetic traits and constantly assessing whether a current generation of SNPs best distinguishes bacteria that are resistant or susceptible to the antimicrobial.

What It Means

These SNPs, or biomarkers can be deployed used to create diagnostic assays that are rapid and deployable into a feedlot setting. In essence these biomarkers and the computational approach provide targets to monitor antimicrobial resistance, guide treatment decisions and a platform that could be used to detect emerging resistance traits.