A Genome‐Scale Metabolic Reconstruction for Klebsiella pneumoniae Kp13

AGUSTÍN PARDO; PABLO RAMOS; MARISA NICOLÁS; EZEQUIEL SOSA; GERMÁN F. BURGUENER; MARCELO MARTI; ADRIAN TURJANSKI; DARÍO FERNÁNDEZ DO PORTO

Rosario

Congreso; XXIII Congreso Latinoamericano de Microbiología; 2016

Sociedad Latinoamericana de Microbiología

Klebsiella pneumoniae is an important opportunistic pathogen associated with nosocomial and community-acquired infections. A wide repertoire of virulence and antimicrobial resistance genes is present in K. pneumoniae genomes, which can constitute extra challenges in the treatment of infections caused by some strains K. pneumoniae. Kp13 is a multidrug-resistant strain responsible for causing a large nosocomial outbreak in a hospital located in Southern Brazil. Its genome was fully sequenced.Genome sequence assembly was performed using Newbler v 2.6 (Roche) and Celera genome assembly v 6.1 (JCV Institute). Gaps within scaffolds were resolved by in silico gap filling. The genome comprises one chromosome (5.3 Mbp) and six plasmids (0.43 Mbp).Genomic annotations were assigned by our own annotation pipeline. The annotation predicted 5799 genes, of which 5688 were coding sequences (ORFs) and 111 structural RNAs (86 tRNAs and 25 rRNAs). A total of 1241 gene products (21,4%) were classified as hypothetical and 87 genes (1,5%) as uncharacterized. We could assign at least one Enzyme Commission number (EC) to 1830 genes (31,6%).Afterwards, we used Pathway tools software and manual revision to build a comprehensive metabolic network (MN) of the bacteria. The software automatically associates genes with reactions, based on EC numbers and function descriptions contained in the GenBank file of the annotated genome. Pathway tools predicted 2307 reactions and 1971 Enzymes assigned to 364 metabolic pathways. Curation includes deleting inappropriate and adding missing pathways and alter them with experimental evidence, or filling existing pathways (by using the hole filler tool of pathway tools).After MN construction, a Python script was developed to generate a list of all products and reactants involved in this network, and the metabolic network was represented as a graph with Cytoscape 2.8.3 4. These allow us to analyze the uniqueness (I.e being a choke point) and centrality of the predicted reactions. As reported, there is significant coincidence between essential genes and genes associated to chokepoints and central reactions.As a last step in our procedure, the annotated genome was deployed in our server: X-OME-Q platform (http://www.biargentina.com.ar/xomeq/). X-ome-Q is an easy-to-use web-based platform which allows genome wide based data consolidation from diverse sources at different processing stages including assembly, annotation, comparative genomics, metabolic pathway recognition, modeling of proteins? structure and experimental data. All this information can be easily navigated by position or by using keywords and different gene based annotations, including ontology and cog terms, protein family and metabolic pathways.We hope that MN reconstruction together with experimental and theoretical data would contribute to predict new effective targets for pathogen drug treatment.