Translating biosynthetic gene cluster architecture into biosensor design for microbial natural product discovery

Antimicrobial resistance (AMR) is a growing global health crisis, with mortality already in the millions and projections of up to 10 million deaths annually by 2050. Traditional discovery strategies, such as one strain many compounds-based screening of large strain collections, have yielded most of our current antibiotics but are slow, resource-intensive, and highly prone to rediscovery. In parallel, high-throughput sequencing has uncovered an enormous reservoir of biosynthetic gene clusters (BGCs), of which only a small fraction has been experimentally characterized, and many show extensive overlap in gene content. This combination of hidden diversity and functional redundancy demands new tools to prioritize, monitor, and rationally activate BGCs. In this review, we discuss how biosensors can be integrated with genome mining to accelerate antimicrobial discovery and BGC dereplication. We summarize the chemical scaffolds, biosynthetic logic and diagnostic enzymatic signatures of major antimicrobial classes, including β-lactams, tetracyclines, macrolides, aminoglycosides, glycopeptides, lincosamides, polyenes, and azoles, and highlighting representative biosensors that target each scaffold. The biosensors discussed use transcription factors, enzymes, aptamers, CRISPR systems and stress-response modules to generate specific and sensitive signals in complex matrices. We argue that translating BGC architecture into biosensor design creates a practical framework to rapidly discriminate from novel activities, but most importantly, to guide the exploration of chemical space around clinically important scaffolds in the era of escalating AMR.