During clonal evolution, every cell accumulates its specific set of consecutive mutations over time. Whereas most mutations have a neutral or deleterious effect on the cellular fitness, occasionally adaptive mutations occur. Often adaptive mutations only drive a fitness increase in the presence of other mutations, a phenomenon referred to as epistasis. When epistasis occurs between mutations in different genes, this is referred to as intergenic epistasis. Intergenic epistasis is prevalent during
adaptive evolution. However, an understanding of how intergenic epistasis contributes to remodeling pathway activities that enable or abrogate the beneficial effect of future mutations is still largely missing. Here we aim at gaining such a pathway-level understanding of intergenic epistasis by combining a dedicated experimental evolution set up with the proper analysis methods.
Key to the analysis is the development of bioinformatics methods that allow exploiting parallelism between independently evolved clones to identify adaptive pathways and to assess the impact of mutations on rewiring the molecular interaction network. As a proof-of-concept the framework will be applied to study the effect of intergenic epistasis on the mode of evolution of bacterial populations subjected to different antibiotic gradients. This will not only provide novel insights from a fundamental point of view, but will also contribute to the optimization of clinical antibiotic treatment.