From Resistance Genes to Resistance States: A Transcriptomics-Driven Precision Medicine Framework for Antimicrobial Therapy

Antimicrobial resistance (AMR) is increasingly addressed through genomic approaches that identify resistance genes and mutations. While these methods have improved surveillance and diagnostics, they often fail to explain patient-specific treatment outcomes, as genetically similar pathogens can exhibit markedly different responses to the same antimicrobial therapy. This discrepancy highlights a fundamental limitation of gene-centric frameworks: resistance is not solely a static genetic property, but a dynamic physiological state shaped by regulatory, metabolic, and environmental factors. This review synthesizes current evidence supporting a transcriptomics-driven perspective of AMR, in which resistance is conceptualized as a context-dependent “resistance state” emerging from regulated gene expression. Pathogen transcriptomics captures functional activity that is invisible to genomic data alone, revealing how transcriptional programs underlying tolerance, persistence, inducible efflux, and stress adaptation contribute to antimicrobial survival without stable genetic change. Experimental and host-relevant studies demonstrate that these transcriptional states are strongly modulated by antibiotic exposure, host immune pressures, infection site physiology, and microbiome context, providing a mechanistic basis for inter-patient variability in treatment response. The review critically examines recent efforts to develop expression-based resistance signatures and discusses the opportunities and limitations of integrating transcriptomics into precision AMR diagnostics. Emphasis is placed on validation requirements, interpretability, and clinical feasibility, as well as on the importance of outcome-linked evidence. Finally, key knowledge gaps and future directions are outlined, including the need for standardized resistance-state definitions, physiologically relevant models, and multicentre clinical validation. By reframing AMR as a dynamic and measurable resistance state, transcriptomics offers a complementary layer to existing diagnostics and a potential pathway toward more precise, individualized antimicrobial therapy.