Employing CRISPR to Identify and Engineer Synergistic Interactions between Antibiotics and Sequence-Specific Treatments

Authors: 
Otoupal, P., University of Colorado Boulder
Erickson, K., University of Colorado Boulder
Chatterjee, A., University of Colorado Boulder
Due to the rise in antibiotic resistance, medical practitioners have turned to combination therapy with the hope of reducing the likelihood of new resistances to emerge. When two treatments act in such a fashion as to potentiate the efficacy of the other, the two are said to be synergistic. Harnessing such interactions results in a more robust treatment, and numerous studies have associated synergy with a reduction in the likelihood of antibiotic resistance emerging.

Just as two antibiotics can interact synergistically, the introduction of either genetic or epigenetic changes alongside standard drug treatments can elicit similar synergistic responses. Numerous CRISPR technologies have been developed to rapidly and efficiently affect such genetic and epigenetic changes. A thorough understanding of the potential synergy (and antagonism) between such modifications and available antibiotics would prove indispensable as it would serve as a powerful guiding force for the design of novel sequence-specific therapies to complement our current arsenal of antimicrobials.

Here, we establish a framework for interrogating such gene-drug synergy by employing gene knockouts and CRISPR interference of bacterial genes. We systematically explored the interactions between 30 specific gene knockouts and nine commonly used antibiotics, and show how this approach can be employed to rapidly characterize synergistic (and antagonistic) interactions. We extend these results to the gene expression landscape and demonstrate how employing deactivated CRISPR-Cas9 constructs to selectively inhibit gene expression replicated these synergistic interactions during simultaneous antibiotic exposure. We further show that combining gene expression inhibitions can exploit inherent epistatic interactions to amplify the degree of synergy with antibiotics. Finally, we show how this synergy potentiates antibiotic activity in an infection model, significantly reducing the bacterial load in HeLa cells infected with Salmonella enterica serovar Typhimurium.

These results demonstrate a new pipeline for finding and designing optimal sequence-specific drug targets that potentiate the activity of existing treatments. Synergy between gene-drug treatments can profoundly impact the ability of microorganisms to escape therapy, and these results highlight the efficacy of these strategies to combat the spread of antibiotic resistance