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Updated: Oct 28, 2026

High-throughput Assay to Phenotype Salmonella enterica Typhimurium Association, Invasion, and Replication in Macrophages
Published on: August 11, 2014
Adaptive Genomic Backgrounds of Antimicrobial-Resistant Salmonella enterica from Chicken Sources: Virulence, Stress
Yihan Li1, Vivienne Song1, Andrew Wang1
1Department of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA.
Background:
Antimicrobial-resistant Salmonella enterica from chicken sources is an important One Health concern because poultry can serve as a route for transmission of resistant foodborne pathogens. This study analyzed whole-genome sequencing data from 4611 chicken-associated Salmonella enterica isolates in the United States from the National Center for Biotechnology Information (NCBI) Pathogen Isolates Browser across seven antimicrobials: tetracycline, streptomycin, sulfisoxazole, ampicillin, ceftriaxone, nalidixic acid, and chloramphenicol.
Methods:
Principal component analysis, hierarchical clustering, and frequency-based bar-plot analysis were used to characterize gene-occurrence patterns and prioritize antimicrobial resistance (AMR), virulence, and stress-response genes. These prioritized genes were evaluated by logistic regression, and genotype-phenotype discrepancy analysis examined resistant isolates lacking expected antimicrobial-specific AMR genes using Fisher's exact tests with Benjamini-Hochberg false discovery rate correction.
Results:
Resistant isolates shared broad genomic features: mdsA and mdsB were the most frequently detected AMR genes across all seven groups; virulence genes including invA, iroB, iroC, sinH, pipA, and avrA were repeatedly prominent; and golS, golT, and asr formed a common stress-response background. In the logistic regression models that predicted the probability that each Salmonella enterica isolate was resistant to a specific antimicrobial based on the presence or absence of selected genes, AMR-only models showed area under the receiver operating characteristic curve (AUC) values of 0.926-0.995, combined virulence and stress-response models showed AUC values of 0.826-0.990, and models combining all three gene categories showed AUC values of 0.935-0.998. Discrepant isolates were uncommon (0.06-2.10%), but enriched virulence and stress-response genes included recurrent pco, sil, spv, and steB signals across multiple antimicrobial groups.
Conclusions:
These findings identify resistance-associated genomic backgrounds involving virulence and stress-response genes and demonstrate their predictive value when incorporated into gene-based models. The enriched genes provide candidates for future genomic and experimental investigations to test whether they contribute functionally to antimicrobial resistance, persistence, or co-selection in poultry-associated Salmonella enterica.
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