An integrated network approach identifies the isobutanol response network of Escherichia coli

Abstract

Isobutanol has emerged as a potential biofuel due to recent metabolic engineering efforts. Here we used gene expression and transcription network connectivity data, genetic knockouts, and network component analysis (NCA) to map the initial isobutanol response network of Escherichia coli under aerobic conditions. NCA revealed profound perturbations to respiration. Further investigation showed ArcA as an important mediator of this response. Quinone/quinol malfunction was postulated to activate ArcA, Fur, and PhoB in this study. In support of this hypothesis, quinonelinked ArcA and Fur target expressions were significantly less perturbed by isobutanol under fermentative growth whereas quinol-linked PhoB target expressions remained activated, and isobutanol impeded growth on glycerol, which requires quinones, more than on glucose. In addition, ethanol, n-butanol, and isobutanol response networks were compared. n-Butanol and isobutanol responses were qualitatively similar, whereas ethanol had notable induction differences of pspABCDE and ndh, whose gene products manage proton motive force. The network described here could aid design and comprehension of alcohol tolerance, whereas the approach provides a general framework to characterize complex phenomena at the systems level.

Correcting direct effects of ethanol on translation and transcription machinery confers ethanol tolerance in bacteria

“…E. coli exposed to ethanol exhibit reduced peptidoglycan cross-linking, which is detrimental to viability, and altered membrane-lipid composition, which may represent an attempt to cope with ethanol stress (10,11). Ethanol induces broad transcriptional changes in E. coli that extend beyond membrane-stress responses (12,13), however, suggesting that membrane effects explain only a part of the toxicity of ethanol. Consistent with this idea, widely varied approaches have successfully been used to achieve modest ethanol tolerance in E. coli, including random transposon insertion (14,15), overexpression of native gene libraries (16)(17)(18), overexpression of protein chaperones (19), engineered oxidation of ethanol (20), transcriptional rewiring through the catabolite activator protein (21) or the transcription factor σ 70 (22), and modulation of cellular fatty-acid composition (23).…”

Metabolic flux analysis of Escherichia coli MG1655 under octanoic acid (C8) stress

“…Reductive stress will lead active Fur which need requires binding ferrous iron (Fe2+) to become active as shown in Figure 32 [53], which is similar with the isobutanol toxicity mechanism. The hypothesis of activation of Fur fits the color difference in biomass hydrolysis shown in following Figure 33 for control and C8 stress pretty well.…”

Advanced biofuel production in microbes

“…The metabolic engineering/systems biology cycle has been successfully applied for the microbial production of 1,3-propanediol [60] and amorphadiene/artemisinic acid [61]. Though this cycle has not yet been applied to the improvement of advanced biofuel production, it should be only a matter of time before we see improvements in biofuel production using this cycle, as papers studying the systems biology of biofuel producing microbes have been recently published [62]. A limitation of the metabolic engineering/systems biology cycle is the time and resource allocation necessary to execute functional genomic studies, integrate the system-wide data into models, and predict potential bottlenecks in microbial biofuel production.…”