Certain environmental microorganisms can cause severe human infections, even in the absence of an obvious requirement for transition through an animal host for replication (“accidental virulence”). To understand this process, we compared eleven isolate genomes of Burkholderia pseudomallei (Bp), a tropical soil microbe and causative agent of the human and animal disease melioidosis. We found evidence for the existence of several new genes in the Bp reference genome, identifying 282 novel genes supported by at least two independent lines of supporting evidence (mRNA transcripts, database homologs, and presence of ribosomal binding sites) and 81 novel genes supported by all three lines. Within the Bp core genome, 211 genes exhibited significant levels of positive selection (4.5%), distributed across many cellular pathways including carbohydrate and secondary metabolism. Functional experiments revealed that certain positively selected genes might enhance mammalian virulence by interacting with host cellular pathways or utilizing host nutrients. Evolutionary modifications improving Bp environmental fitness may thus have indirectly facilitated the ability of Bp to colonize and survive in mammalian hosts. These findings improve our understanding of the pathogenesis of melioidosis, and establish Bp as a model system for studying the genetics of accidental virulence.
The structure of BPSL1549, a protein of unknown function from Burkholderia pseudomallei, reveals a similarity to Escherichia coli cytotoxic necrotizing factor 1. We found that BPSL1549 acted as a potent cytotoxin against eukaryotic cells and was lethal when administered to mice. Expression levels of bpsl1549 correlate with conditions expected to promote or suppress pathogenicity. BPSL1549 promotes deamidation of glutamine-339 of the translation initiation factor eIF4A, abolishing its helicase activity and inhibiting translation. We propose to name BPSL1549 Burkholderia lethal factor 1.
Burkholderia pseudomallei is the causative agent of melioidosis, an often fatal infectious disease for which there is no vaccine. B. pseudomallei is listed as a tier 1 select agent, and as current therapeutic options are limited due to its natural resistance to most antibiotics, the development of new antimicrobial therapies is imperative. To identify drug targets and better understand the complex B. pseudomallei genome, we sought a genome-wide approach to identify lethal gene targets. As B. pseudomallei has an unusually large genome spread over two chromosomes, an extensive screen was required to achieve a comprehensive analysis. Here we describe transposon-directed insertion site sequencing (TraDIS) of a library of over 106 transposon insertion mutants, which provides the level of genome saturation required to identify essential genes. Using this technique, we have identified a set of 505 genes that are predicted to be essential in B. pseudomallei K96243. To validate our screen, three genes predicted to be essential, pyrH, accA, and sodB, and a gene predicted to be nonessential, bpss0370, were independently investigated through the generation of conditional mutants. The conditional mutants confirmed the TraDIS predictions, showing that we have generated a list of genes predicted to be essential and demonstrating that this technique can be used to analyze complex genomes and thus be more widely applied.
Current and reoccurring viral epidemic outbreaks such as those caused by the Zika virus illustrate the need for rapid development of antivirals. Such development would be facilitated by computational approaches that can provide experimentally testable predictions for possible antiviral strategies. To this end, we focus here on the fact that viruses are directly dependent on their host metabolism for reproduction. We develop a stoichiometric, genome-scale metabolic model that integrates human macrophage cell metabolism with the biochemical demands arising from virus production and use it to determine the virus impact on host metabolism and vice versa. While this approach applies to any host–virus pair, we first apply it to currently epidemic viruses Chikungunya, Dengue and Zika in this study. We find that each of these viruses causes specific alterations in the host metabolic flux towards fulfilling their biochemical demands as predicted by their genome and capsid structure. Subsequent analysis of this integrated model allows us to predict a set of host reactions, which, when constrained, inhibit virus production. We show that this prediction recovers known targets of existing antiviral drugs, specifically those targeting nucleotide production, while highlighting a set of hitherto unexplored reactions involving both amino acid and nucleotide metabolic pathways, with either broad or virus-specific antiviral potential. Thus, this computational approach allows rapid generation of experimentally testable hypotheses for novel antiviral targets within a host.
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