GSE122806
Changes in gene expression predictably shift and switch genetic interactions
An important goal in disease genetics and evolutionary biology is to understand how mutations combine together to alter phenotypes and fitness.  Non-additive genetic (epistatic) interactions between mutations occur extensively within and between genes, which makes accurate genetic prediction a difficult challenge.  Moreover, for unclear reasons, the interactions between mutations change quite extensively across conditions, cell types, and species, with important consequences for both evolution and precision medicine such as the exploitation of synthetic lethality in cancer. To better understand the plasticity of genetic interactions, we reduced the problem to a minimal system where we combined mutations within a single protein performing a single cellular function. The only perturbation to the system was a change in the expression level of the mutated gene itself. Even in this minimal system, the interactions between mutations were highly plastic, with interactions changing in both magnitude and sign when the expression level was altered. Mathematical modelling revealed the cause of this as the non-linear relationship between the concentration of the protein and the cellular phenotype.These non-linearities are widespread in biology and transform expression level-independent effects of mutations on protein folding and stability into mutation outcomes and interactions that shift and switch as gene expression changes. This plasticity of mutation effects and genetic interactions has important implications for human disease and evolutionary theory. Overall design: Individual and double mutational effects were analyzed within the same gene (lambda repressor CI helix turn helix domain; GeneID: 3827059) at two protein expression levels (high and low). Mutated region: 52nd-210th nucleotide bases. The mutational effects were analysed based on their ability to repress expression of GFP from the PR promoter on plasmid constructs (based on pBADM-11 vector, lambda repressior was expressed with inducible arabinose-controled pBAD promoter, and GFP under the control of PR promoter regulated by lambda repressor). Both expression-level experiments were performed in three biological replicates. The library was sequenced before functional selection (Input as a control, also with three biological replicates). Selection was by flourescent-activated cell sorting, with each replicate matching each input replicate. Two output populations were sequenced: output1 corresponds to the cell population carrying functional genetic variants (repressing the target gene GFP expression levels to wild type protein level) and output 2 corresponds to the cell population with intermediate target gene expression levels.