Learning from the Past: What Can the Genome of Beef Feedlot Environments Tell Us About Antimicrobial Resistance?

Background

Globally and within Canada there have been many projects focused on using DNA sequencing to investigate the microbial communities in beef feedlots though analyzed with different methodology due to researcher preference, lack of access to technologies, software limitations or over-simplifications. With a recent push to publicly share raw and processed DNA data, this team has the opportunity to go back with current knowledge to potentially understand more about antimicrobial resistance (AMR) across feedlots in North America. For these DNA-sequence based methods to have value to producers, a common unit to quantify AMR genes (ARGs) is required to understand the levels of ARGs in the environment and how that is correlated to feedlot health. This study seeks to make the first step through a unified and retrospective dive of bacterial whole genome and metagenome data from animals and environments associated with North American beef cattle feedlots.

Objectives

• Generate a North American feedlot-specific list of ARGs
• Perform quantification and comparison of ARGs based on sample types.

What they will do

This team will use data publicly available through the National Centre for Biotechnology Information (NCBI) to achieve their objectives. This data set includes whole genomes of bacteria that has been collected historically from cattle, humans, and the environment. The team will determine a criteria for which sequences will be included in the study vs. removed which will include rules that make sure all samples are directly or in-directly associated with North American beef cattle production.

After the dataset has been acquired and appropriately filtered, the Ruzzini lab will perform an analysis. First, by reporting the prevalence of specific ARGs by the pathogen they are associated with, including a comparison of BRD pathogens from North American and around the world in the past two decades. Secondly, they will use the data set to look at the levels of ARGs in the metagenomes from animals and feedlot environments to identify the key players for the industry.

Implications

This study serves as a means to bring the research of the last 20 years together to understand the burden of ARGs on Canadian beef feedlots. Ideally, these datasets will provide a list that informs on the “Top 100” or even “Top 20” ARGs that exist in these environments and the role they are playing in disease and AMR to improve animal health and economics at the feedlot level.