Global Metabolomic and Network analysis of <i>Escherichia coli</i> Responses to Exogenous Biofuels

Although synthetic biology progress has made it possible to produce various biofuels in more user-friendly hosts, such as <i>Escherichia coli</i>, the large-scale biofuel production in these non-native systems is still challenging, mostly due to the very low tolerance of these non-native hosts to the biofuel toxicity. To address the issues, in this study we determined the metabolic responses of <i>E. coli</i> induced by three major biofuel products, ethanol, butanol, and isobutanol, using a gas chromatography–mass spectrometry (GC–MS) approach. A metabolomic data set of 65 metabolites identified in all samples was then subjected to principal component analysis (PCA) to compare their effects and a weighted correlation network analysis (WGCNA) to identify the metabolic modules specifically responsive to each of the biofuel stresses, respectively. The PCA analysis showed that cellular responses caused by the biofuel stress were in general similar to aging cells at stationary phase, inconsistent with early studies showing a high degree of dissimilarity between metabolite responses during growth cessation as induced through stationary phases or through various environmental stress applications. The WGCNA analysis allowed identification of 2, 4, and 2 metabolic modules specifically associated with ethanol, butanol, and isobutanol treatments, respectively. The biofuel-associated modules included amino acids and osmoprotectants, such as isoleucine, valine, glycine, glutamate, and trehalose, suggesting amino acid metabolism and osmoregulation are among the key protection mechanisms against biofuel stresses in <i>E. coli</i>. Interestingly, no module was found associated with all three biofuel products, suggesting differential effects of each biofuel on <i>E. coli</i>. The findings enhanced our understanding of <i>E. coli</i> responses to exogenous biofuels and also demonstrated the effectiveness of the metabolomic and network analysis in identifying key targets for biofuel tolerance

Global Metabolomic and Network analysis of <i>Escherichia coli</i> Responses to Exogenous Biofuels

Although synthetic biology progress has made it possible to produce various biofuels in more user-friendly hosts, such as <i>Escherichia coli</i>, the large-scale biofuel production in these non-native systems is still challenging, mostly due to the very low tolerance of these non-native hosts to the biofuel toxicity. To address the issues, in this study we determined the metabolic responses of <i>E. coli</i> induced by three major biofuel products, ethanol, butanol, and isobutanol, using a gas chromatography–mass spectrometry (GC–MS) approach. A metabolomic data set of 65 metabolites identified in all samples was then subjected to principal component analysis (PCA) to compare their effects and a weighted correlation network analysis (WGCNA) to identify the metabolic modules specifically responsive to each of the biofuel stresses, respectively. The PCA analysis showed that cellular responses caused by the biofuel stress were in general similar to aging cells at stationary phase, inconsistent with early studies showing a high degree of dissimilarity between metabolite responses during growth cessation as induced through stationary phases or through various environmental stress applications. The WGCNA analysis allowed identification of 2, 4, and 2 metabolic modules specifically associated with ethanol, butanol, and isobutanol treatments, respectively. The biofuel-associated modules included amino acids and osmoprotectants, such as isoleucine, valine, glycine, glutamate, and trehalose, suggesting amino acid metabolism and osmoregulation are among the key protection mechanisms against biofuel stresses in <i>E. coli</i>. Interestingly, no module was found associated with all three biofuel products, suggesting differential effects of each biofuel on <i>E. coli</i>. The findings enhanced our understanding of <i>E. coli</i> responses to exogenous biofuels and also demonstrated the effectiveness of the metabolomic and network analysis in identifying key targets for biofuel tolerance