Foodborne pathogens pose substantial health hazards and result in considerable economic losses in the U.S. Fortunately, the National Center for Biotechnology Information Pathogen Detection Isolates Browser (NPDIB) provides valuable access to antimicrobial resistance (AMR) genes and antimicrobial assay data. This study aimed to conduct the first comprehensive investigation of AMR genes in pathogens isolated from U.S. cattle over the past decade, driven by the urgent need to address the dangers of AMR specifically originating in pathogens isolated from U.S. cattle. In this study, around 28,000 pathogen isolate samples were extracted from the NPDIB and then analyzed using multivariate statistical methods, mainly principal component analysis (PCA) and hierarchical clustering (H-clustering). These approaches were necessary due to the high dimensions of the raw data. Specifically, PCA was utilized to reduce the dimensions of the data, converting it to a two-dimensional space, and H-clustering was used to better identify the differences among data points. The findings from this work highlighted Salmonella enterica and Escherichia coli as the predominant pathogens among the isolates, with E. coli being the more concerning pathogen due to its increasing prevalence in recent years. Moreover, tetracycline was observed as the most commonly resistant antimicrobial, with the resistance genes mdsA, mdsB, mdtM, blaEC, and acrF being the most prevalent in pathogen isolates from U.S. cattle. The occurrence of mdtM, blaEC, acrF, and glpT_E448k showed an increase in pathogens isolated from U.S. cattle in recent years. Furthermore, based on the data collected for the locations of AMR cases, Texas, California, and Nebraska were the major areas carrying major AMR genes or antimicrobials with detected resistance. The results from this study provide potential directions for targeted interventions to mitigate pathogens' antimicrobial resistance in U.S. cattle.
Keywords: antimicrobial resistance; cattle; data analysis; foodborne pathogens; principal component analysis.