Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis

Abstract

Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.

Figure 1. Network model characteristics.

(a) Properties of the updated PAO1 model as compared to previously published GEMs for P. aeruginosa, iMO1056 and iMO1096, as well as properties of the new PA14 model. (b) The number of genes, metabolites and reactions in mPA14 grouped into functional categories as defined by KEGG (ref. 74). For the distribution of genes, metabolites and reactions in mPAO1, see Supplementary Fig. 1.

Figure 2. Visualization of experimental virulence-linked essential genes.

Distribution of virulence-linked genes and growth-essential genes from experiments in CF sputum visualized across all mPA14 reactions (grey) using MetDraw. Reactions associated with virulence-linked genes (as defined by the Pseudomonas Genome Database) are highlighted in red, and reactions associated with genes essential to growth in synthetic cystic fibrosis sputum are shown in blue. Purple reactions are associated with both virulence and growth essentiality. All reactions and metabolites are labelled with unique identifiers referenced in the model, visible at high magnification and text-searchable.

Figure 3. Genes essential for VF synthesis versus growth in SCFM.

The table lists the 46 genes essential for growth and production of at least one VF. Pathway assignment via PseudoCAP annotation and tabulated count of VFs for which the gene is essential are also included. Impact of a given gene's deletion is shown as white indicating 0% inhibition and black indicating 100% inhibition.

Figure 4. VF synthesis and growth interconnectivity.

(a) In silico gene knockouts were performed and the subsequent levels of biomass and VF production for each VF in the model were measured. The amount of growth inhibition was calculated by normalizing the knockout biomass production to the wild-type biomass production. Likewise, the amount of VF synthesis inhibition was calculated by normalizing the mutant level of VF production to the wild-type level of VF production. Each point indicates the growth inhibition (x axis) and VF inhibition (y axis) relative to wild-type for a given in silico knockout. All data points are transparent such that a high density of data points results in an increase in colour intensity. Coloured circles are used to indicate genes of interest as labelled in the pyoverdine example with yellow representing genes involved in amino acid metabolism, green carbohydrate metabolism, dark blue energy metabolism and light blue VF metabolism. (b) We highlight genes representing unique subtypes of impact on pyoverdine synthesis versus growth in a quantitative way that enables easy comparison of the activity of these genes across all VFs, with white indicating 0% inhibition and black indicating 100% inhibition.

Figure 5. Pyoverdine synthesis capabilities in vitro on SCFM.

PA14 wild-type and pvdA, hom and gapA PA14 mutants were grown to stationary phase in SCFM and growth was measured using OD600 (a). Subsequently, the supernatants were isolated and the OD405 of each condition's supernatant was measured as a proxy for pyoverdine levels. The OD405 was divided by the OD600 of the culture in order to normalize pyoverdine production to growth (b). Error bars indicate s.d. among five biological replicates.