Investigation of the Antimicrobial Resistance of Important Pathogens Isolated from Poultry from 2015 to 2023 in the United States

Foodborne pathogens cause around 47.8 million illnesses in the U.S. annually, with antimicrobial misuse in food production, particularly in poultry processing, contributing significantly to this public health challenge. Misuse of antimicrobials can contribute to antimicrobial resistance (AMR) and make the treatment of pathogens increasingly difficult. This emphasizes the need to investigate antimicrobial resistance in U.S. poultry. This study analyzes data from the NCBI Pathogen Isolates Browser (2015-2023) to explore the relationships between antimicrobial-resistant pathogens, AMR genes, and antimicrobials detected with resistance in pathogens isolated from chicken and turkey. Using principal component analysis and hierarchical clustering, we mapped and profiled regional and temporal patterns of antimicrobial resistance. Salmonella enterica was the most prevalent antimicrobial-resistant pathogen across both chicken and turkey, with notable outbreaks, particularly in the Northeast. Antimicrobial-resistant Campylobacter jejuni was more prevalent in chicken, particularly in California and Georgia, while Escherichia coli and Shigella were more prominent in turkey, with concentrated antimicrobial resistance in Texas for pathogen samples isolated from chicken. Resistance to tetracycline and streptomycin was widespread, with distinct regional clusters: antimicrobial resistance was concentrated in states like Minnesota for pathogens isolated from chicken, while AMR found in pathogens isolated from turkey was more evenly distributed across the Midwest. Key AMR genes, such as tet(A), mdsA, and mdsB, also followed similar patterns, peaking in 2019 and significantly declining by 2022. The observed decline in AMR cases may be linked to improved biosecurity measures and disruptions in detection due to the COVID-19 pandemic. This comprehensive study of antimicrobial resistance in U.S. poultry provides valuable insights into resistance trends, which provide useful information to inform targeted interventions and policies to mitigate AMR threats in the poultry production industry. For consumers, these findings emphasize the importance of proper food handling and cooking practices to reduce the risk of exposure to resistant pathogens. Regulatory authorities should focus on enforcing stricter antimicrobial usage policies and enhancing surveillance systems to sustain the reduction in AMR cases.

Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability-the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand-protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets.

A Comprehensive Study of Historical Detection Data for Pathogen Isolates from U.S. Cattle

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.

Protein Interaction Network Analysis to Investigate Stress Response, Virulence, and Antibiotic Resistance Mechanisms in Listeria monocytogenes

Listeria monocytogenes is a deadly and costly foodborne pathogen that has a high fatality rate in the elderly, pregnant women, and people with weakened immunity. It can survive under various stress conditions and is a significant concern for the food industry. In this work, a data analysis approach was developed with existing tools and databases and used to create individual and combined protein interaction networks to study stress response, virulence, and antimicrobial resistance and their interaction with L. monocytogenes. The networks were analyzed, and 28 key proteins were identified that may serve as potential targets for new strategies to combat L. monocytogenes. Five of the twenty-eight proteins (i.e., sigB, flaA, cheA, cheY, and lmo0693) represent the most promising targets because they are highly interconnected within the combined network. The results of this study provide a new set of targets for future work to identify new strategies to improve food preservation methods and treatments for L. monocytogenes.

Investigation of the Genes Involved in the Outbreaks of Escherichia coli and Salmonella spp. in the United States

Salmonella spp. and Escherichiacoli (E. coli) are two of the deadliest foodborne pathogens in the US. Genes involved in antimicrobial resistance, virulence, and stress response, enable these pathogens to increase their pathogenicity. This study aims to examine the genes detected in both outbreak and non-outbreak Salmonella spp. and E. coli by analyzing the data from the National Centre for Biotechnology Information (NCBI) Pathogen Detection Isolates Browser database. A multivariate statistical analysis was conducted on the genes detected in isolates of outbreak Salmonella spp., non-outbreak Salmonella spp., outbreak E. coli, and non-outbreak E. coli. The genes from the data were projected onto a two-dimensional space through principal component analysis. Hierarchical clustering was then used to quantify the relationship between the genes in the dataset. Most of the outlier genes identified in E. coli isolates are virulence genes, while outlier genes identified in Salmonella spp. are mainly involved in stress response. Gene epeA, which encodes a high-molecular-weight serine protease autotransporter of Enterobacteriaceae (SPATE) protein, along with subA and subB that encode cytotoxic activity, may contribute to the pathogenesis of outbreak E. coli. The iro operon and ars operon may play a role in the ecological success of the epidemic clones of Salmonella spp. Concurrent relationships between esp and ter operons in E. coli and pco and sil operons in Salmonella spp. are found. Stress-response genes (asr, golT, golS), virulence gene (sinH), and antimicrobial resistance genes (mdsA and mdsB) in Salmonella spp. also show a concurrent relationship. All these findings provide helpful information for experiment design to combat outbreaks of E. coli and Salmonella spp.