Active gut microbiota resistome identified by metatranscriptomics and its contribution to antimicrobial resistance

Abstract

Abstract The global health crisis of antimicrobial resistance (AMR) is compounded by gut microbiota, which acts as a reservoir for antibiotic-resistant genes (ARGs). While DNA-based metagenomics can identify the potential for resistance, the resistome, it cannot determine the ARGs effectively contributing towards resistance. Unlike broader reviews of gut resistomes or microbiome functional profiling, this review proposes a potential–activity–phenotype–translation framework that distinguishes ARG detection at the DNA level from transcriptional activity, experimentally validated resistance, and clinically relevant outcomes. This framework positions the active resistome as a dynamic and context-dependent intermediate between the genetic reservoir of resistance and observable antimicrobial-resistance phenotypes. This review aims to summarize the current knowledge on “active resistome”, the transcriptionally active component of the gut resistome, by exploring the applications of metatranscriptomics to profile ARG expression in the gut. Metatranscriptomics studies transcript-level data to identify active AMR mechanisms. Further, strain-level transcript linkage is achieved by matched metagenomes. This RNA-level sequencing provides valuable insights into real-time activity of resistance mechanisms like horizontal gene transfer, β-lactamases, and efflux pumps in response to host and environmental factors, including probiotics and antibiotic exposure. It also provides clinical applications and public health implications, such as the potential of active resistomes as a diagnostic biomarker of AMR risks, including therapeutic failure and recurrent infections. This is coupled with systematic antibiotic stewardship programs (ASPs) to lower inappropriate antibiotic consumption and combat AMR. We also address the field’s current challenges of technical biases, lack of standardization, and difficulty in linking RNA expression to its phenotype, while outlining future directions like AI/ Machine Learning for ARG prediction, multiomics integration, and multidisciplinary cohort studies. In conclusion, a comprehensive understanding of the gut’s active resistome is crucial to the development of personalized and effective strategies to combat the global threat of antimicrobial resistance.