Genetic Markers of Antimicrobial Resistance in Experimental Models and Surveillance Systems

Abstract

Background . Antimicrobial resistance (AMR) has high medical and social significance, as it is shaped by clinical, veterinary, agricultural, and environmental processes, as well as horizontal transfer of mobile genetic elements. Its analysis requires the assessment of individual resistance genes, experimental models, diagnostic approaches, as well as surveillance systems that track antibiotic resistance genes (ARGs) across human, animal, and environmental reservoirs. The aim of the study was to summarize data on experimental and computational models of ARG dissemination, characterize selected AMR markers, and evaluate current methods for their detection. Material and methods . A narrative review with a transparent search strategy and thematic synthesis was conducted. The search included PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar; the last search was performed on May 2, 2026. After full-text assessment, 46 sources were included in the review. Results. The review discusses blaCTX-M-15, blaNDM, blaKPC, blaOXA-23, floR and intI1 markers, as well as mobile genetic elements involved in their dissemination. It summarizes the strengths and limitations of in vitro models, gut models, mouse models, biofilm systems, mathematical modelling, AST, PCR/qPCR, WGS, mNGS, machine learning, and CRISPR-Cas12a diagnostics. Conclusion . CRISPR-Cas12a platforms may support rapid targeted screening of predefined ARGs but do not replace phenotypic susceptibility testing or genome-scale analysis. The most robust strategy is the integration of AST, WGS/mNGS, targeted molecular diagnostics, machine learning, and One Health surveillance.