bioRxivSystems BiologyPreprint
Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance
Kevin RycheliD, Justin TaniD, Arjun PateliD, Cameron R. Lamoureux, Ying HefneriD, Richard SzubiniD, Josefin JohnseniD, Elsayed T. MohamediD, Patrick V. PhaneufiD, Amitesh AnandiD, Connor A. OlsoniD, Joon Ho ParkiD, Anand V. SastryiD, Laurence YangiD, Adam M. FeistiD, Bernhard Ørn PalssoniD
Abstract
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 reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: E. coli
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- Citations
- 3
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