Explores CRISPR–Cas delivery strategies and challenges to combat antimicrobial resistance, indicating potential advancements in treatment.
Antimicrobial resistance (AMR) is a major global health threat that demands precise and targeted alternatives to conventional antibiotics. CRISPR–Cas systems have emerged as programmable tools capable of selectively targeting resistance genes, virulence factors, and essential bacterial sequences, enabling sequence-specific killing or re-sensitization of multidrug-resistant pathogens. This article examines key CRISPR-based antimicrobial strategies, including plasmid curing, gene disruption, and base editing, with emphasis on their functional outcomes in clinically relevant bacteria. Particular attention is given to delivery platforms, which remain the primary bottleneck for effective application. Approaches such as engineered bacteriophages, conjugative and broad host-range plasmids, nanoparticle-based systems, and extracellular vesicles are evaluated based on delivery efficiency, host specificity, and performance in complex microbial environments. Recent innovations, including biomimetic vesicles and CRISPR-associated transposase systems, further demonstrate the expanding capabilities of targeted genome manipulation. The utility of CRISPR-based systems in diagnostics is also highlighted, particularly for rapid and sensitive detection of antimicrobial resistance genes, supporting improved surveillance strategies. However, several challenges limit clinical translation, including inefficient delivery in vivo, off-target effects, immune responses, and the potential emergence of CRISPR-resistant bacterial variants. Future progress will depend on optimizing delivery systems, improving targeting specificity through advanced guide RNA design, and integrating computational tools for resistance monitoring. With continued development, CRISPR–Cas technologies offer a highly adaptable platform for precise antimicrobial intervention and hold strong potential for addressing the growing burden of AMR.
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Deepan et al. (2026) studied this question.
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