Genome-scale metabolic reconstructions of multiple
            <i>Escherichia coli</i>
            strains highlight strain-specific adaptations to nutritional environments

Shigella flexneri proliferate in infected human epithelial cells at exceptionally high rates. This vigorous growth has important consequences for rapid progression to life-threatening bloody diarrhea, but the underlying metabolic mechanisms remain poorly understood. Here, we used metabolomics, proteomics, and genetic experiments to determine host and Shigella metabolism during infection in a cell culture model. The data suggest that infected host cells maintain largely normal fluxes through glycolytic pathways, but the entire output of these pathways is captured by Shigella, most likely in the form of pyruvate. This striking strategy provides Shigella with an abundant favorable energy source, while preserving host cell ATP generation, energy charge maintenance, and survival, despite ongoing vigorous exploitation. Shigella uses a simple three-step pathway to metabolize pyruvate at high rates with acetate as an excreted waste product. The crucial role of this pathway for Shigella intracellular growth suggests targets for antimicrobial chemotherapy of this devastating disease.infectious diseases | host-pathogen interactions

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Shigella reroutes host cell central metabolism to obtain high-flux nutrient supply for vigorous intracellular growth

Kentner

Martano

Callon

et al. 2014

“…network reconstructions have proven to be powerful tools to probe the genetic diversity of metabolism between organisms (9) and among strains within a species (10,11). As useful as genome annotation is, it does not provide an understanding of the integrated function of gene products to produce phenotypic states.…”

Section: Significancementioning

confidence: 99%

“…Similarly, the metabolic capabilities of all strains were combined to define the full potential of metabolic capabilities for the S. aureus species, or its panmetabolic network (Fig. 4A) (10).…”

Section: Known S Aureus Virulence Factors Are Unequally Distributedmentioning

confidence: 99%

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

Bosi

Monk

Aziz

et al. 2016

Self Cite

211

196

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.systems biology | mathematical modeling | pathogenicity | core genome | pangenome

“…As a preliminary expansion of this study, the conservation of the newly discovered isozymes in 55 closely related strains of E. coli and Shigella that have existing metabolic models was investigated (39). It was determined that the same GPR changes should be made in the majority of these models.…”

mentioning

confidence: 99%

Model-driven discovery of underground metabolic functions in Escherichia coli

Guzmán¹,

Utrilla²,

Nurk

et al. 2015

Self Cite

Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli-aspC, argD, and gltA-are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.underground metabolism | substrate promiscuity | systems biology | isozyme discovery | genome-scale modeling