IAMBEE enabled to identify several genes and pathways important for increasing ethanol tolerance in E. coli. The aim is to use forward genetics to unravel the function of these mutations in ethanol tolerance, identify the mechanisms by which higher tolerances is established in the cells and to use these genetic markers in industrially relevant strains, such as Zymomonas mobilis in order to improve their ethanol production capacity.
Genetic determinants of ethanol tolerance in E. coli
Understanding the relationship between an organisms genotype and phenotype remains one of biology’s most fundamental challenges. Over the past six decades, geneticists have focused primarily on the simplest questions: what is the precise role of a particular gene or genetic locus, and how does variation in this locus affects the phenotype of an organism? While this research yielded invaluable fundamental insight and enabled powerful industrial and medical applications, it is only the tip of the proverbial genetic iceberg. It becomes increasingly clear that many phenotypes and diseases depend on multiple mutations (alleles) in multiple genes (loci). Moreover, in many cases, there is a complex interaction between these mutations - a specific mutation may only play a role when other mutations are also present. Complex genetic interactions are present in all forms of life and are responsible for several industrially and medically relevant properties.
In our lab we use experimental evolution to study the genetic determinants underlying high ethanol tolerance in Escherichia coli. Even though understanding and improving this trait is vital for strain engineering, it has been challenging to fully elucidate the underlying mechanisms. Previous studies have identified single genes as well as epistatically interacting genes involved in higher ethanol tolerance. However, tolerance to ethanol is clearly a complex trait established by the interaction of multiple genes and pathways and a broad understanding of ethanol tolerance in E. coli is currently lacking.
To cope with the complexity of the mutational dataset resulting from our evolution experiments, we developed in collaboration with Prof. Kathleen Marchal (UGent, Belgium) a computational tool, named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). This tool is network-based and uses relevance scores to rank mutations based on their predicted relevance in the observed phenotype (Figure 1).
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