Abstract
Antibiotic combinations are considered a relevant strategy to tackle the global antibiotic resistance crisis since they are believed to increase treatment efficacy and reduce resistance evolution (WHO treatment guidelines for drug-resistant tuberculosis: 2016 update.). However, studies of the evolution of bacterial resistance to combination therapy have focused on a limited number of drugs and have provided contradictory results (Lipsitch, Levin BR. 1997; Hegreness et al. 2008; Munck et al. 2014). To address this gap in our understanding, we performed a large-scale laboratory evolution experiment, adapting eight replicate lineages of Escherichia coli to a diverse set of 22 different antibiotics and 33 antibiotic pairs. We found that combination therapy significantly limits the evolution of de novode novo resistance in E. coli, yet different drug combinations vary substantially in their propensity to select for resistance. In contrast to current theories, the phenotypic features of drug pairs are weak predictors of resistance evolution. Instead, the resistance evolution is driven by the relationship between the evolutionary trajectories that lead to resistance to a drug combination and those that lead to resistance to the component drugs. Drug combinations requiring a novel genetic response from target bacteria compared with the individual component drugs significantly reduce resistance evolution. These data support combination therapy as a treatment option to decelerate resistance evolution and provide a novel framework for selecting optimized drug combinations based on bacterial evolutionary responses.
Highlights
The prevalence of antibiotic resistance has become a global health concern, limiting the efficacy of standard treatments for acute and chronic bacterial infections (Ventola 2015)
Resistance Evolution towards a Diverse Set of Antibiotic Combinations To identify the underlying features that drive the evolution of resistance to combination therapy, we adapted genetically barcoded replicate lineages (Jahn et al 2018) of the wellstudied model organism E. coli K12 MG1655 to a diverse set of 22 different antibiotics and 33 different antibiotic pairs
The classification was done by measuring the drug concentration that resulted in a 90% growth-reduction (IC90) of the wild type (WT) compared with WT growth in media only for all single antibiotics and for the antibiotics in combination
Summary
The prevalence of antibiotic resistance has become a global health concern, limiting the efficacy of standard treatments for acute and chronic bacterial infections (Ventola 2015). As the development of novel antibiotics is expensive in terms of time and resources (Luepke et al 2017), it is important to use currently available drugs in the best possible way to decelerate antibiotic resistance evolution and to maximize positive treatment outcomes. Empiric combination therapy is believed to improve treatment outcomes via increased potency and reduced evolution of drug resistance (Blomberg et al 2001; Bantar et al 2004). The clinical benefit of combination therapy remains controversial (Leibovici et al 1997; Bantar et al 2004; Bliziotis et al 2005; Paul 2014; Skorup et al 2014; Tepekule et al 2017; Lipcsey et al 2018). The disparate results might be explained by an incomplete understanding of the factors that drive the evolution of resistance to combination therapy. Drug combinations have been mainly studied in regards to phenotypic characteristics, such as drug interaction (Hegreness et al 2008; Torella et al 2010; Munck et al 2014; Baym et al 2016; Barbosa et al 2018) or collateral drug responses (Munck et al 2014; de Evgrafov et al 2015)
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