Abstract
Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100–200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.
Highlights
Metabolic network reconstructions are systems biology tools that capture in one framework the function of all known genes, proteins, and reactions within the metabolic network of an organism (Palsson, 2011)
Simulations using the most recent version of the E. coli metabolic model (Orth et al, 2011) suggest that aldA should be an essential gene in glucose M9 media; the aldA mutant is viable experimentally in this medium (Supplementary Figure S1)
We use an inconsistency between simulation and experimental data to drive new biological insight, finding that aldA and prpC form a synthetic lethal (SL) pair, and to investigate whether this finding might translate into a biomedical application
Summary
Metabolic network reconstructions are systems biology tools that capture in one framework the function of all known genes, proteins, and reactions within the metabolic network of an organism (Palsson, 2011). The conversion of a metabolic reconstruction into a computational format allows one to simulate flux states of the network that correspond biologically to different phenotypes, and thereby to computationally investigate the genotype–phenotype relationship for an organism. The ability to simulate different phenotypes distinguishes computational models from static maps of biochemical pathways. The latter provides a pictorial diagram of all pathways in a network but no information on their usage or activity levels in a living organism, while the former provides information regarding which pathways are active under the simulation condition. Metabolic models have already been used to aid strain design for metabolic engineering (Lee et al, 2005; Perez Pulido et al, 2005; Park et al, 2011; Licona-Cassani et al, 2012), to analyze network properties (Almaas et al, 2005; Nam et al, 2012), and to provide context for the analysis of highthroughput omics data (Chandrasekaran and Price, 2010; Chang et al, 2010; van Berlo et al, 2011), and their role in such projects is anticipated to grow as the models are refined and simulate a larger number of biological conditions more accurately
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