Antibiotic resistance is a rising worldwide medical concern, and multi-drug treatments are becoming increasingly important in combating the spread of drug-resistant bacterial pathogens. The impact of multi-drug combinations on the evolution of resistance depends critically on the level of synergy or antagonism between the drugs. In particular, we recently showed that suppressive (hyper- antagonistic) drug interactions, in which the combined effect of two drugs is smaller than the effect of one of the drugs alone, can lead to selection against resistance. In a particularly strong suppressive drug interaction in Escherichia coli, antibiotics inhibiting translation relieve part of the reduction of bacterial growth caused by inhibitors of DNA synthesis. Although suppression between drugs profoundly slows down or even inverts the evolution of resistance, the underlying mechanisms that lead to such suppressive drug interactions are not understood. Here, we propose an experimental approach for identifying the genetic determinants of suppressive drug interactions. We will test the hypothesis that non-optimal regulation of ribosomal genes under DNA stress leads to higher than optimal overall protein synthesis, which in turn causes translation inhibiting drugs to be beneficial. Specifically, we will (a) use GFP-tagged transcription reporters to measure expression of ribosomal and other genes under DNA synthesis inhibitors, protein synthesis inhibitors, and antibiotics with other modes of action, as well as under combinations of these antibiotics. We will identify how bacteria resolve the conflict between two antibiotic stress signals that individually elicit an opposite gene expression response. (b) We will genetically modify the expression of ribosomal genes to correct the presumed imbalance between DNA and protein synthesis under DNA synthesis inhibiting drugs. We will identify genetic modifications to transcription regulation that are more optimized for survival under DNA stress. Further, we will explore whether such genetic optimization can reduce or even remove the suppressive drug interaction. We anticipate that these results will point to a regulatory genetic determinant for suppressive drug interactions. These insights will be key to understanding how drug interactions may change due to mutations and selection.
Antibiotics are the most direct and effective approach available against many infectious diseases, but their usefulness is being undermined by the emergence and spread of drug-resistant pathogens. A novel strategy for combining antibiotics relies on drug interactions to reduce, and perhaps even reverse, the spread of drug resistance while providing an effective treatment paradigm to combat disease. Our research will reveal the mechanism underlying these important drug-drug interactions.
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