A Gapless, Unambiguous Genome Sequence of the Enterohemorrhagic Escherichia coli O157:H7 Strain EDL933.

Escherichia coli EDL933 is the prototypic strain for enterohemorrhagic E. coli serotype O157:H7, associated with deadly food-borne outbreaks. Because the publicly available sequence of the EDL933 genome has gaps and >6,000 ambiguous base calls, we here present an updated high-quality, unambiguous genome sequence with no assembly gaps

Exploiting Adaptive Laboratory Evolution of Streptomyces clavuligerus for Antibiotic Discovery and Overproduction

Adaptation is normally viewed as the enemy of the antibiotic discovery and development process because adaptation among pathogens to antibiotic exposure leads to resistance. We present a method here that, in contrast, exploits the power of adaptation among antibiotic producers to accelerate the discovery of antibiotics. A competition-based adaptive laboratory evolution scheme is presented whereby an antibiotic-producing microorganism is competed against a target pathogen and serially passed over time until the producer evolves the ability to synthesize a chemical entity that inhibits growth of the pathogen. When multiple Streptomyces clavuligerus replicates were adaptively evolved against methicillin-resistant Staphylococcus aureus N315 in this manner, a strain emerged that acquired the ability to constitutively produce holomycin. In contrast, no holomycin could be detected from the unevolved wild-type strain. Moreover, genome re-sequencing revealed that the evolved strain had lost pSCL4, a large 1.8 Mbp plasmid, and acquired several single nucleotide polymorphisms in genes that have been shown to affect secondary metabolite biosynthesis. These results demonstrate that competition-based adaptive laboratory evolution can constitute a platform to create mutants that overproduce known antibiotics and possibly to discover new compounds as well

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity

Systems biology-guided identification of synthetic lethal gene pairs and its potential use to discover antibiotic combinations

Mathematical models of metabolism from bacterial systems biology have proven their utility across multiple fields, for example metabolic engineering, growth phenotype simulation, and biological discovery. The usefulness of the models stems from their ability to compute a link between genotype and phenotype, but their ability to accurately simulate gene-gene interactions has not been investigated extensively. Here we assess how accurately a metabolic model for Escherichia coli computes one particular type of gene-gene interaction, synthetic lethality, and find that the accuracy rate is between 25% and 43%. The most common failure modes were incorrect computation of single gene essentiality and biological information that was missing from the model. Moreover, we performed virtual and biological screening against several synthetic lethal pairs to explore whether two-compound formulations could be found that inhibit the growth of Gram-negative bacteria. One set of molecules was identified that, depending on the concentrations, inhibits E. coli and S. enterica serovar Typhimurium in an additive or antagonistic manner. These findings pinpoint specific ways in which to improve the predictive ability of metabolic models, and highlight one potential application of systems biology to drug discovery and translational medicine