4.0 Article

A curated C-difficile strain 630 metabolic network: prediction of essential targets and inhibitors

期刊

BMC SYSTEMS BIOLOGY
卷 8, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s12918-014-0117-z

关键词

Essential genes; Flux balance analysis; Manual curation; Reconstructed metabolic network; Synthetic accessibility; Inhibitors; Cross-reactivity targets

资金

  1. Faculty of Medicine, Universite de Sherbrooke
  2. PROTEO (the Quebec network for research on protein function, structure and engineering)
  3. Fonds de Recherche du Quebec en Sante (FRQ-S)
  4. CREMUS graduate fellowship from the Faculty of Medicine, Universite de Sherbrooke

向作者/读者索取更多资源

Background: Clostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. Current treatments for Clostridium difficile infections present high relapse rates and new hyper-virulent and multi-resistant strains are emerging, making the study of this nosocomial pathogen necessary to find novel therapeutic targets. Results: We present iMLTC806cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC806cdf contains 806 genes, 703 metabolites and 769 metabolic, 117 exchange and 145 transport reactions. iMLTC806cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 89.2% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs (the closest organism for which such data exists). We predict the existence of 76 essential genes and 39 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 29 of these potential therapeutic targets, we find 125 inhibitors of homologous proteins including approved drugs with the potential for drug repositioning, that when validated experimentally could serve as starting points in the development of new antibiotics. Conclusions: We created a highly curated metabolic network model of C. difficile strain 630 and used it to predict essential genes as potential new therapeutic targets in the fight against Clostridium difficile infections.

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