Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism

Abstract

A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm metabolism. Our simulation results showed that plausible, non-intuitive explanations of difficult-to-interpret experimental observations could be generated by integrating genome-scale kinetic models with gene expression profiles.

Fig 1. Alteration of biofilm formation by different reaction inhibitions in the histidine and purine synthesis pathways.

Reactions indicated with magenta and cyan arrows were experimentally identified by Musken et al. [22]. We predicted reactions indicated with red and green arrows. Abbreviations: aicar, 5-phosphoribosyl-4-carbamoyl-5-aminoimidazole; prpp, 5-phosphoribosyl diphosphate; amp, adenylate. See S1 Supporting Information for the definition of the remaining of the abbreviations.

Fig 2. Expression of pqs, psl, and pel operons as a function of biofilm age.

(A) Correlation coefficient of the expression intensity of the genes in the pqs, psl, and pel operons and biofilm age (h). (B) Expression of one gene from each operon as a function of biofilm age. Gene expressions are shown in log₂ scale. Each dot corresponds to one condition in the dataset of gene expression for P. aeruginosa PAO1 biofilms. The circles correspond to the data obtained by Costaglioli et al. [11]. AU, arbitrary units; h, hours.

Fig 3. Coordinated regulation of Psl synthesis.

(A) Sketch of the metabolic pathways involved in the increase of Psl and Pel production rates in our simulations. The figure shows the genes associated with the nine reactions that had the highest effect on Psl production. The number to the left of each gene name denotes the rank of the corresponding reaction. The number in parentheses denotes the overall gene expression ratio between the biofilm and the stationary cultures. The numbers to the left and right of the vertical bar denote the median flux ratios of the reactions associated with the genes in simulation with or without the gene expression ratios of the genes accABCD, fabD, purADF, pgl, edd, gcd, and rmlA. Only psl, pel, and those genes whose regulation contributes to increasing Psl and Pel production are shown. (B) Correlation between the expression of the genes that contribute to increasing Psl production and the psl operon genes for biofilm and stationary cultures. Solid and dashed arrows indicate single and multiple reaction steps in the model, respectively.

Fig 4. Dysregulation of secondary metabolites related to biofilm formation by inhibition of anthranilate degradation.

(A) Sketch of the metabolic pathways involved in anthranilate and chorismate metabolism. Metabolite names written in red were predicted to increase when the reactions marked with a red x, which correspond to the low biofilm producers identified by Costaglioli et al. [11], were inhibited. (B) Correlation of the gene expression intensity of genes associated with anthranilate- and chorismate-derived secondary metabolites, psl and pel genes, with the genes of the kynurenine pathway.

Fig 5. Definition of metabolite sets whose concentration changes were specific to either biofilm-reducing or biofilm-increasing reactions.

The defined sets are as follows: set 1, metabolites that specifically increased when inhibiting biofilm-reducing reactions; set 2, metabolites that specifically decreased when inhibiting biofilm-reducing reactions; set 3, metabolites that specifically increased when inhibiting biofilm-increasing reactions; and set 4, metabolites that decreased when inhibiting biofilm-increasing reactions. Note that each metabolite set includes metabolites from two subsets of the Venn diagram. The number in parentheses indicates the number of metabolites in each subset.