Paper Info
Title
Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction.
Abstract
Background   Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets. Results  We reconstructed the genome-scale metabolic network of B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets. Conclusions  As the first genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.
Year
DOI
Venue
2011
10.1186/1752-0509-5-83
BMC systems biology
Keywords
Field
DocType
algorithms,immune system,drug targeting,bioinformatics,antibiotic resistance,catalysis,iterative reconstruction,phenotype,biomass,genome,enzyme,metabolic network,systems biology,genome annotation,computational biology
Antibiotic resistance,Genome,Biology,Systems biology,Metabolic network,Microbiology,Bioinformatics,Virulence,Burkholderia cenocepacia,Pseudomonas aeruginosa,Pathogen
Journal
Volume
Issue
ISSN
5
null
1752-0509
Citations 
PageRank 
References 
9
0.46
10
Authors
10
Name
Order
Citations
PageRank
Kechi Fang190.79
Hansheng Zhao290.46
Changyue Sun390.46
Carolyn M C Lam490.79
Suhua Chang51057.18
Kunlin Zhang6274.08
Gurudutta Panda790.46
Miguel Godinho8282.37
Vítor A P Martins dos Santos9456.63
Jing Wang1010910.35