Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

Automated summary

The relationships between the genome, transcriptome, and metabolome play a crucial role in determining the characteristics of an organism. However, it has been challenging to study these relationships due to the complexity of the variables involved. A new data analytic method called iModulons has been developed to simplify the interpretation of the transcriptome by grouping genes into independently regulated sets. In this study, the researchers used iModulons along with resequencing and metabolic models to gain a deep understanding of the effects of causal mutations and metabolic rewiring in E. coli strains subjected to high levels of oxidative stress. The findings highlight the power of iModulon knowledge mapping in evolutionary analysis.