Dataset Information

Larocque2014 - Metabolic reconstruction of Clostridium difficile pathogenic strain 630 (iMLTC806cdf)

ABSTRACT: In the current work, we present the first extensively curated metabolic network reconstruction for C. difficile (strain 630) i MLTC806cdf and validate it with experimental data on essential metabolites and carbon sources. We compare i MLTC806cdf to existing networks showing the importance of manual curation and using the network to predict essential genes. The predictions agree with experimental data for B. subtilis (the closest organism for which such data is available). We detect potential cross-reactivity targets for each of these genes using a variety of methods combining systems and structural computational biology and determine that for only 4 out of 76 predicted essential genes, if exiting, the potential human cross-reactivity targets are themselves essential in the human metabolic network reconstruction RECON2. For a number of essential genes, we find potential binding small molecules, including approved drugs such as Ribavirin, which may inhibit the respective gene products. We hope that i MLTC806cdf will find further use in the community and the results here lead to the development of novel antibiotics against C. difficile infections.

SUBMITTER: Rafael Najmanovich

PROVIDER: MODEL1409240004 | BioModels | 2021-11-03

REPOSITORIES: BioModels

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

Larocque Mathieu M Chénard Thierry T Najmanovich Rafael R

BMC systems biology 20141015

<h4>Background</h4>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.<h4>Results</h4>We present iMLTC806cdf, an extensively curated reconstructed metabol ...[more]

PMID: 25315994

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