Foodborne pathogens cause thousands of illnesses across the US each year. However, these pathogens gain resistance to the antimicrobials that are commonly used to treat them. Typically, antimicrobial resistance is caused by mechanisms encoded by multiple antimicrobial-resistance genes. These are carried through pathogens found in foods such as meats. It is, thus, important to study the genes that are most related to antimicrobial resistance, the pathogens, and the meats carrying antimicrobial-resistance genes. This information can be further used to correlate the antimicrobial-resistance genes found in humans for improving human health. Therefore, we perform the first multivariate statistical analysis of the antimicrobial-resistance gene data provided in the NCBI Pathogen Detection Isolates Browser database, covering six states that are geographically either in close proximity to one another (i.e., Pennsylvania (PA), Maryland (MD), and New York (NY)) or far (i.e., New Mexico (NM), Minnesota (MN), and California (CA)). Hundreds of multidimensional data points were projected onto a two-dimensional space that was specified by the first and second principal components, which were then categorized with a hierarchical clustering approach. It turns out that aadA, aph(3''), aph(3'')-Ib, aph(6)-I, aph(6)-Id, bla, blaCMY, tet, tet(A), and sul2 constructed the assembly of ten genes that were most commonly involved in antimicrobial resistance in these six states. While geographically close states like PA, MD and NY share more similar antimicrobial-resistance genes, geographically far states like NM, MN, and CA also contain most of these common antimicrobial-resistance genes. One potential reason for this spread of antimicrobial-resistance genes beyond the geographic limitation is that animal meats like chicken and turkey act as the carriers for the nationwide spread of these genes.
Keywords: antimicrobial-resistance genes; foodborne pathogens; hierarchical clustering; principal component analysis.