Antibiotic resistance can evolve through sequential accumulation of multiple mutations1. To study such gradual evolution, we developed a selection device, the morbidostat, which continuously monitors bacterial growth and dynamically regulates drug concentrations such that the evolving population is constantly challenged. We analyzed evolutionary trajectories of Escherichia coli populations towards resistance to chloramphenicol, doxycycline, and trimethoprim. Over a period of ~20 days, resistance levels increased dramatically, with parallel populations showing similar phenotypic trajectories. Whole-genome sequencing revealed both drug-specific and drug-general genetic changes. Chloramphenicol and doxycycline resistance evolved through diverse combinations of mutations in genes involved in translation, transcription, and transport2. In contrast, trimethoprim resistance evolved in a stepwise manner1,3, through mutations restricted to the target enzyme dihydrofolate reductase (DHFR)4,5. Sequencing DHFR over time revealed that parallel populations not only evolved similar mutations, but also acquired them in similar order6. Uncovering such recurrent genotypic pathways may help the spread of resistance.

The development and spread of antibiotic resistance in bacteria is a universal threat to both humans and animals that is generally not preventable, but can nevertheless be controlled and must be tackled in the most effective ways possible. To explore how the problem of antibiotic resistance might best be addressed, a group of thirty scientists from academia and industry gathered at the Banbury Conference Centre in Cold Spring Harbor, New York, May 16-18, 2011. From these discussions emerged a priority list of steps that need to be taken to resolve this global crisis.

Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.