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

Vital-Lopez FG, Reifman J, Wallqvist A - PLoS Comput. Biol. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America.

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.

No MeSH data available.


Related in: MedlinePlus

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.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4592021&req=5

pcbi.1004452.g005: 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.

Mentions: For the inhibition of a metabolic reaction, we defined a scoring function to count how many metabolite concentration changes were in common with the concentration changes caused by inhibition of reactions associated with genes whose mutations are known to either reduce or increase biofilm formation [22] (see S3 Table for the list of the genes). We considered that a metabolite had a considerable concentration change if the median change was larger than two-fold and all the changes occurred in the same direction across the ensemble of reference flux distributions. Based on the predicted metabolite concentration changes caused by the inhibition of the biofilm-reducing and biofilm-increasing reactions, we defined four metabolite sets as follows (see Fig 5):


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

Vital-Lopez FG, Reifman J, Wallqvist A - PLoS Comput. Biol. (2015)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4592021&req=5

pcbi.1004452.g005: 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.
Mentions: For the inhibition of a metabolic reaction, we defined a scoring function to count how many metabolite concentration changes were in common with the concentration changes caused by inhibition of reactions associated with genes whose mutations are known to either reduce or increase biofilm formation [22] (see S3 Table for the list of the genes). We considered that a metabolite had a considerable concentration change if the median change was larger than two-fold and all the changes occurred in the same direction across the ensemble of reference flux distributions. Based on the predicted metabolite concentration changes caused by the inhibition of the biofilm-reducing and biofilm-increasing reactions, we defined four metabolite sets as follows (see Fig 5):

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America.

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.

No MeSH data available.


Related in: MedlinePlus