Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics

• Main Authors: Cheng, Allen A. (Contributor), Ding, Huiming (Contributor), Lu, Timothy K. (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor), Massachusetts Institute of Technology. Synthetic Biology Center (Contributor)
• Format: Article
• Language: English
• Published: National Academy of Sciences (U.S.), 2015-03-03T19:29:59Z.
• Subjects: Article
• Online Access: Get fulltext

New therapeutic strategies are needed to treat infections caused by drug-resistant bacteria, which constitute a major growing threat to human health. Here, we use a high-throughput technology to identify combinatorial genetic perturbations that can enhance the killing of drug-resistant bacteria with antibiotic treatment. This strategy, Combinatorial Genetics En Masse (CombiGEM), enables the rapid generation of high-order barcoded combinations of genetic elements for high-throughput multiplexed characterization based on next-generation sequencing. We created ~34,000 pairwise combinations of Escherichia coli transcription factor (TF) overexpression constructs. Using Illumina sequencing, we identified diverse perturbations in antibiotic-resistance phenotypes against carbapenem-resistant Enterobacteriaceae. Specifically, we found multiple TF combinations that potentiated antibiotic killing by up to 10[superscript 6]-fold and delivered these combinations via phagemids to increase the killing of highly drug-resistant E. coli harboring New Delhi metallo-beta-lactamase-1. Moreover, we constructed libraries of three-wise combinations of transcription factors with >4 million unique members and demonstrated that these could be tracked via next-generation sequencing. We envision that CombiGEM could be extended to other model organisms, disease models, and phenotypes, where it could accelerate massively parallel combinatorial genetics studies for a broad range of biomedical and biotechnology applications, including the treatment of antibiotic-resistant infections.
National Institutes of Health (U.S.) (New Innovator Award DP2 OD008435)
United States. Office of Naval Research
Ellison Medical Foundation (New Scholar in Aging Award)
Henry L. and Grace Doherty Charitable Foundation