Modern medicine depends on antibiotics to treat infections by disabling targets inside bacterial cells. Once inside these cells, antibiotics bind to certain sites on specific enzyme targets to stop bacterial growth. Randomly occurring changes (mutations) in the genes for these targets occur naturally, in some cases making the target harder for the antibiotic to attach to and that bacterial version resistant to treatment.
For this reason, the more antibiotics have been used over time, the greater the chances that bacterial populations will evolve to have mutants resistant to existing antibiotics, and the more urgent the call for new approaches to keep the treatments from becoming obsolete. Researchers have for decades studied resistant mutants in hopes that related mechanisms would guide the design of new treatments to overcome resistance. The effort has been limited, however, because naturally occurring resistant mutants represent a small fraction of the mutations that could possibly occur (the complete mutational space), with most drug binding site mutations to date having been overlooked.
To address this challenge, a new study led by researchers at NYU Grossman School of Medicine applied a technology called MAGE (multiplex automated genome engineering) to generate the full inventory of mutations in the bacterial species Escherichia coli where the antibiotic rifampicin attaches to and disables an essential bacterial enzyme known as RNA polymerase (RNAP). The study authors created 760