• Interpreting Antimicrobial Resistance in Veterinary Pathogens: Lessons from Systems Biology and Single-Cell Analysis
  • Seyedeh Fatemeh Angoshtan,1,*
    1. Department of clinical Sciences, Faculty of Veterinary Medicine, Semnan University, Semnan, Iran


  • Introduction: Antimicrobial Resistance (AMR) in veterinary pathogens represents threats to animal health, food security, and human health, via mechanisms of zoonotic transmission. In this regard, there is emerging multidrug resistance in bacterial pathogens of pets and livestock resulting from the widespread application of antimicrobials. Conventional bulk analyses obscure the heterogeneity and dynamic nature of resistance and hinder effective intervention strategies. Systems biology and single-cell technologies offer transformational capabilities for unraveling molecular and ecological drivers of AMR at high resolution to provide precision stewardship and diagnostics in the One Health approach. The purpose of this review is to compile recent innovations in systems biology and single-cell analysis to elucidate the mechanisms of AMR in veterinary pathogens. Specifically, this review will highlight how integrating omics and single-cell profiling can elucidate resistance networks, phenomena of phenotypic heterogeneity, and host-pathogen interactions, facilitating new diagnostics and therapies.
  • Methods: We conducted a systematic search of the peer-reviewed literature published between 2015-2025 with keywords, including antimicrobial resistance, veterinary pathogens, systems biology, and single-cell analysis. There was a search for studies using multi-omics (genomics, transcriptomics, proteomics, metabolomics) and single-cell-based methods.
  • Results: Systems biology indicates AMR is the result of complex interactions in addition to genetic mutations, including regulatory feedback loops, metabolic reprogramming, and environmental initiators. Multi-omics approaches have revealed new biomarkers for AMR, including S. aureus efflux pump regulators in bovine mastitis, and stress-induced transcriptional switches in Campylobacter jejuni from poultry. Single-cell studies have also demonstrated heterogeneity in response to AMR, providing evidence of persistence occurring due to antibiotic resistance being transiently acquired through stochastic gene expression or through metabolic dormancy. For example, scRNA-seq in Salmonella from poultry has identified subpopulations with different patterns of drug susceptibility, while microfluidics has tracked real-time resistance in swine E. coli. Mobile genetic elements such as plasmids and integrons aid the dissemination of resistance related to host determinants such as cytokines and gut metabolites across microbial communities. These findings challenge binary resistance classifications and highlight the need for dynamic, cell-resolved diagnostics.
  • Conclusion: Single-cell analysis and systems biology provide a substantial framework to address AMR complexity in veterinary pathogens. The graphical representation of both resistance networks and cellular heterogeneity within this framework allows researchers to implement next-generation diagnostics, like single-cell biosensors, and targeted therapies like phage therapy, while also providing the basis for antimicrobial stewardship in establishing intervention points to mitigate resistance dissemination. Future research must apply machine learning to assess AMR trends and conduct a longitudinal study on multiple species to bolster One Health approaches to appropriate stewardship of AMR in both veterinary and human medicine.
  • Keywords: Antimicrobial resistance, veterinary pathogens, systems biology, single-cell analysis