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.

Figure 1

Schematic representation of the metabolic network in B. cenocepacia J2315, referred as model iKF1028.

Figure 2

Metabolic pathways included in iKF1028 and the distribution of gene-associated and non-gene-associated reactions for each pathway.

Figure 3

Specific structure of Lipopolysaccharide (LPS) in B. cenocepacia J2315 and the synthesis pathways of LPS as well as the proteins involved. The lipid A portion of LPS is composed of two linked glucosamine residues (purple hexagon) with fatty acid side chains (wavy lines), (R)-3-hydroxyhexadecanoic (C16:0 (3-OH)) in an amide linkage and (R)-3-hydroxytetradecanoic (C14:0 (3-OH)) acid and tetradecanoic acid (C14:0) in an ester linkage. There are 4-amino-4-deoxyarabinose (Ara4N, brown sphere) moieties attached to the phosphate residues in the lipid A backbone. The inner core oligosaccharide contains unusual KDO-KO-Ara4N residue linked to the lipid A (KDO: 3-deoxy-D-manno-octulosonic acid, dark blue hexagon; KO: D-glycero-D-talo-octulosonic acid, light blue hexagon). Various polysaccharides comprise the outer core oligosaccharide (L-glycero-D-manno-heptose, blue heptagon; glucose, dark green hexagon; galactose, light green hexagon; quinovosamine, orange hexagon; rhamnose, red hexagon). J2315 cannot make complete LPS O-antigen, owing to an insertion element in BCAL3125 [47].

Figure 4

Gene essentiality analysis. (a) Distribution of essential genes predicted on M9 and SCFM respecively; (b) Overlapping essentail genes among in silico prediction on M9, SCFM, and essential genes with in vivo evidence from two P. aeruginosa strains: P. aeruginosa PAO1 and P. aeruginosa PA14.

Figure 5

The process for genome-scale metabolic reconstruction of B. cenocepacia J2315. The left side indicates resources used for reconstruction, and the right side indicates the reconstruction process. Initial reconstruction started from genome annotation and other biological databases. Gap-filling was a continuous step throughout the reconstruction by probing missing reactions in a pathway which causes in silico growth infeasible, and subsequently closing these gaps by referring to the biological databases, extensive literature mining, and comparison with BIOLOG substrate utilization assays [89,90]. This improved model was then extended by adding key metabolic virulence factors for B. cenocepacia from the literature. The process of model development and validation against experimental data was iteratively repeated until the genome-scale metabolic model was robust.