Final Report Summary - NETWORK EVOLUTION (Integrated evolutionary analyses of genetic and drug interaction networks in yeast)
Understanding how evolution of microbial resistance towards a given antibiotic influences susceptibility to other compounds is a challenge of profound importance for several fields of basic and applied research. Despite its obvious clinical importance, our knowledge is still limited, not least because this problem has only been addressed so far by isolated case studies. No large-scale, systematic laboratory study has been devoted to investigate the evolution of bacterial cross-resistance and hypersensitivity under controlled laboratory conditions. To our best knowledge, the only prior work with similar aims was published 60 years ago and was limited to phenomenological descriptions (Szybalski and Bryson 1952).
For the first time, we applied an integrated systems biology approach to decipher cross- resistance s between antibiotics using the bacterium Escherichia coli. We combined tools of i) High-throughput laboratory evolutionary experiments, ii) whole-genome sequencing of laboratory-evolved resistant strains, and iii) Chemoinformatics and computational biology. The main novelties are as follows: First, we present the first experimental map of cross-resistance and collateral sensitivity interactions (i.e. antibiotic pairs A and B are connected if evolutionary adaptation to A changes sensitivity to B or vice versa). Second, the network is consistent with large-scale co-resistance data from clinical isolates of the same species. To our best knowledge, this is the first time when results of laboratory experimental evolution and clinical data are systematically compared and show good agreement. Thus, this network could serve as a unique resource and potentially permit informed decisions in medicine. Third, by analysing this map, we deliver, for the first time, general principles governing the evolution of cross-resistance patterns. By integrating available data on antibiotic properties, we demonstrate that these patterns are predictable. This result paves the way towards in silico methods to infer the cross-resistance propensity of novel antimicrobial compounds. Fourth, we identified a molecular mechanism underlying hypersensitivity of antibiotic-resistant bacteria to other agents. This result could aid the rational design of sequential multi-drug therapies. Finally, many central issues in evolutionary biology rely on our understanding of trade-offs in adaptation. The framework presented in this paper could guide future works to infer the general rules and molecular underpinnings of evolutionary trade-offs. Our work shows that it is possible to predict rather than simply interpret patterns of evolution. Based on the above points, we believe that our work will be of broad interest.
For the first time, we applied an integrated systems biology approach to decipher cross- resistance s between antibiotics using the bacterium Escherichia coli. We combined tools of i) High-throughput laboratory evolutionary experiments, ii) whole-genome sequencing of laboratory-evolved resistant strains, and iii) Chemoinformatics and computational biology. The main novelties are as follows: First, we present the first experimental map of cross-resistance and collateral sensitivity interactions (i.e. antibiotic pairs A and B are connected if evolutionary adaptation to A changes sensitivity to B or vice versa). Second, the network is consistent with large-scale co-resistance data from clinical isolates of the same species. To our best knowledge, this is the first time when results of laboratory experimental evolution and clinical data are systematically compared and show good agreement. Thus, this network could serve as a unique resource and potentially permit informed decisions in medicine. Third, by analysing this map, we deliver, for the first time, general principles governing the evolution of cross-resistance patterns. By integrating available data on antibiotic properties, we demonstrate that these patterns are predictable. This result paves the way towards in silico methods to infer the cross-resistance propensity of novel antimicrobial compounds. Fourth, we identified a molecular mechanism underlying hypersensitivity of antibiotic-resistant bacteria to other agents. This result could aid the rational design of sequential multi-drug therapies. Finally, many central issues in evolutionary biology rely on our understanding of trade-offs in adaptation. The framework presented in this paper could guide future works to infer the general rules and molecular underpinnings of evolutionary trade-offs. Our work shows that it is possible to predict rather than simply interpret patterns of evolution. Based on the above points, we believe that our work will be of broad interest.