Modeling of Potentially Virulence-Associated Metabolic Pathways in Pseudomonas Aeruginosa PA14 Including Experimental Verification | AIChE

Modeling of Potentially Virulence-Associated Metabolic Pathways in Pseudomonas Aeruginosa PA14 Including Experimental Verification

Authors 

Dräger, A. - Presenter, Center for Bioinformatics Tübingen (ZBIT)
Renz, A., University of Tübingen
Bohn, E., University Hospital Tübingen
Schütz, M., University Hospital Tübingen
According to the report of the ‘Antimicrobial resistance surveillance in Europe (2015),’ P. aeruginosa is an opportunistic, human pathogen that causes many infections in hospitalized patients with immune defects or impairments. Since it is difficult to control P. aeruginosa in hospitals, it can cause hospital-acquired pneumonia [1]. Some strains of P. aeruginosa seem to be associated with higher mortality than others. With the help of a published genome-scale model of PA14 [2] and sequencing data of a highly pathogenic patient strain that was recently isolated in Tübingen, metabolic differences between the laboratory and patient strain shall be identified and subsequently verified in a laboratory experiment.


First, differences in the sequences of the two strains were identified by performing SNP analysis. These differences were then used to find metabolic alterations affecting the virulence. By using FBA and in-silico gene knock-out, three genes were identified that could be responsible for the difference in pathogenicity between the laboratory and the highly pathogenic patient strain. These genes affect metabolic reactions that are associated with virulence in P. aeruginosa. Gene knock-outs of these three genes are momentarily being performed in a laboratory experiment to verify their metabolic relevance in virulence.


However, only a fraction of the genes of P. aeruginosa is included in the published model. Many additional genes are associated with virulence, differ between the sequenced laboratory and patient strains, or both. Therefore, the model is being extended to increase its predictive value.

  1. European Centre for Disease Prevention and Control. Antimicrobial resistance surveillance in Europe 2015. Annual Report of the European Antimicrobial Resistance Surveillance Network (EARS-Net). Stockholm: ECDC; 2017. https://ecdc.europa.eu/en/publications-data/antimicrobial-resistance-surveillance-europe-2015#no-link
  2. Bartell JA, Blazier AS, Yen P, Thøgersen JC, Jelsbak L, Goldberg JB, and Papin JA. Reconstruction of the metabolic network of Pseudomonsas aeruginosato interrogate virulence factor synthesis, Nature Communications (2017). doi:10.1038/ncomms14631