Abstracts (first author)
A single molecule assay to study the fitness effects of leaky gene expression in individual cellsPDF
Most of the adaptive substitutions that are revealed in evolution experiments are in regulatory sequences. In many cases, the expression levels are under directional selection. The lac operon in Escherichia coli is a good model system to investigate the fitness effects of the different regulation precisions (tight or leaky regulation) under different conditions. LacI, the repressor of the lac operon, can bind to three different operators with different binding strengths (O1>O2>O3). Yet, LacI can bind even stronger to the artificial operator sequence, Osym. Thus, the evolutionary optimal case may not necessarily be the tightest regulation of the lac operon. Our hypothesis is that the individuals with lac operon leakage will have a fitness disadvantage due to the cost of the unnecessary expression of lac operon when there is no lactose to utilize, but if lactose suddenly becomes available those individuals will benefit from previous leakage and have a higher fitness than the individuals with no leakage. Bulk growth assays can only assess fitness differences that are due to differences in genotype. Unless one looks at the single cell level it is not possible to reveal the contribution of stochastic aspects of the phenotype to observed fitness differences between isogenic populations. We do this by correlating leakage events in the lac operon of an individual E. coli in lactose free medium, with its fitness before and after the switch to lactose medium. The leakage events are quantified using single molecule fluorescence microscopy where we can count the number of lactose permease molecules per cell (Choi 2008). The leakage in expression is monitored in cells growing in a microfluidic turbidostat (Ullman 2013). Here, we can follow growth of thousands of individual cells with high time resolution and switch between different growth media. This enables us to relate minute differences in fitness to the underlying stochastic differences in gene expression.