Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene-and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.

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Pathogen-host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.

High conservation of the disease-associated genes between flies and humans facilitates the common use ofDrosophila melanogasterto study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model ofDrosophilausing an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded viaDrosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene–reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resultingDrosophilamodel (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson’s disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

High conservation of the disease-associated genes between fly and human facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the orthology-based draft model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Further, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and updated various metabolic pathways including cholesterol metabolism. The performance of the resulting Drosophila model, termed iDrosophila1 (8,230 reactions, 6,990 metabolites, and 2,388 genes), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated transcriptome-based prediction capacity of the iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate systems-level metabolic alterations in response to genetic and environmental perturbations.