Abstract:
Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance.
However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways.
Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As
adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal
passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a
novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE
exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional
implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to
search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored
2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in
identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not
only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel
systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the
temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the
molecular underpinnings of other complex traits as well.
