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Reconstruction of polymicrobial cross-talk network

The initial aim of this task was to integrate existing information on P. aeruginosa and C. albicans regulation and antimicrobial sucseptibility testing. Later, this reconstruction will also integrate the newly generated gene expression profiles.

These networks link genes to biological processes and molecular functions, and notably characterise the pathways responsible for the production of virulence and resistance factors (such as QS molecules). Moreover, they represent experimental results on testing different antimicrobial agents over QS and other virulence related factors.

By overlaying the gene expression profiles generated over these networks it will be possible to identify which virulence and resistance factors are being expressed and which other regulatory genes are playing a part in infection, and species communication. Also, by comparing the expression profiles of mono and polymicrobial biofilms, we may look into changes on the expression of VFs.

Both P. aeruginosa and C. albicans have available dedicated knowledge bases, and some efforts have been made to model their regulatory systems, namely to identify major virulence and resistance regulatory cascades. Available P. aeruginosa reconstructions depict key VF modules, such as the biosynthesis of alginate, rhamnolipids and phenazines, antibiotic resistance and QS systems (Galán-Vásquez et al., 2011), and host-pathogen interactions (Zhang et al., 2012). C. albicans reconstructions address various pathogenesis features, namely: iron uptake during adhesion and invasion into human oral epithelial cells (Linde et al., 2010), cell adhesion and biofilm formation (Nobile et al., 2012), host-pathogen interactions (Tierney et al., 2012), and how morphology reflects the infectious behaviour on host tissues (Kuo et al., 2013). Additional information was retrieved from databases such as: the organism-centric repositories Pseudomonas Genome Database (Winsor et al., 2011) and Candidas Genome Database (Skrzypek et al., 2016); databases on virulence and resistance factors such as the Virulence Factors of Pathogenic Bacteria Database (VFDB) (L. Chen et al., 2012) and the Antibiotic Resistance Genes Database (ARDB) (Liu and Pop, 2009), and the Online Gene Essentiality Database (OGEE) gene essentiality database (W.-H. Chen et al., 2012).


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  • Chen,W.-H. et al. (2012) OGEE: an online gene essentiality database. Nucleic Acids Res., 40, D901–6.
  • Galán-Vásquez,E. et al. (2011) The Regulatory Network of Pseudomonas aeruginosa. Microb. Inform. Exp., 1, 3.
  • Kuo,Z.-Y. et al. (2013) Identification of infection- and defense-related genes via a dynamic host-pathogen interaction network using a Candida albicans-zebrafish infection model. J. Innate Immun., 5, 137–152.
  • Linde,J. et al. (2010) Regulatory network modelling of iron acquisition by a fungal pathogen in contact with epithelial cells. BMC Syst. Biol., 4, 148.
  • Liu,B. and Pop,M. (2009) ARDB--Antibiotic Resistance Genes Database. Nucleic Acids Res., 37, D443–7.
  • Nobile,C.J. et al. (2012) A recently evolved transcriptional network controls biofilm development in Candida albicans. Cell, 148, 126–38.
  • Skrzypek,M.S. et al. (2016) How to Use the Candida Genome Database. Methods Mol. Biol., 1356, 3–15.
  • Tierney,L. et al. (2012) An Interspecies Regulatory Network Inferred from Simultaneous RNA-seq of Candida albicans Invading Innate Immune Cells. Front. Microbiol., 3, 85.
  • Winsor,G.L. et al. (2011) Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes. Nucleic acids …, 39, D596–600.
  • Zhang,M. et al. (2012) Prediction and Analysis of the Protein Interactome in Pseudomonas aeruginosa to Enable Network-Based Drug Target Selection. PLoS One.