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.

ThePseudomonas aeruginosaaccessory genome elements influence virulence towardsCaenorhabditis elegans

“…corresponding to the core genome and the set of genes that occur only once in these strains (referred to as 'singletons', 90 Figure 1B). The ratio between the pangenome and the core genome (5.6) agrees with a previously reported ratio: 5.3 (van 91 Belkum et al, 2015), confirming that P. aeruginosa harbors a large amount of strain-specific variation in protein-coding 92…”

“…Supplemental Table 1) and belong to three different subtypes, that can be absent/present independently of each other: type 20 I-F (73%), type I-E (35%) and I-C (21%). This distribution of CRISPR-Cas systems is consistent and similar to previous 21 surveys of P. aeruginosa CRISPR-Cas systems (van Belkum et al, 2015). In addition to the genomic presence of 22 CRISPR-Cas loci, we also investigated if the identified CRISPR-Cas systems were predicted to be active or inactive based 23 on the presence/absence of known anti-CRISPR genes.…”

Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria

“…We analyzed an existing metabolic model for P. aeruginosa (Bartell et al , 2017) using previously published Tn-Seq and RNA-Seq profiles for 18 antibiotics (Murray et al , 2015) and a metabolic shift from in vitro minimal media to in vivo wound infection (Turner et al , 2014). These data used two different strains of P. aeruginosa : PA14 for the antibiotic experiments and PAO1 for the metabolic studies.…”

The global regulator Crc orchestrates the metabolic robustness underlying oxidative stress resistance in Pseudomonas aeruginosa

“…6). The activity of the ED pathway was assessed by a combined assay for Edd (6-phosphogluconate dehydratase) and Simplified representation of the central carbon metabolism in P. aeruginosa PAO1 as deduced from the Pseudomonas database (Winsor et al, 2016) and information from the literature (Bartell et al, 2017). Changes in the transcriptome (T) and proteome (P) when comparing the Δcrc mutant versus the wild-type strain in exponential cultures in LB medium are shown for specific nodes in the network using coloured dots.…”