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A Boolean model of the Pseudomonas syringae hrp regulon predicts a tightly regulated system.

MacLean D, Studholme DJ - PLoS ONE (2010)

Bottom Line: The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease.Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole.We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected.

View Article: PubMed Central - PubMed

Affiliation: The Sainsbury Laboratory, John Innes Centre, Norwich, United Kingdom. dan.maclean@tsl.ac.uk

ABSTRACT
The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease. Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole. In order to gain a better understanding of these dynamical behaviours and to create a basis for the refinement of the experimentally derived knowledge we created a Boolean model of the regulatory interactions within the hrp regulon of Pseudomonas syringae pathovar tomato strain DC3000 Pseudomonas syringae. We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected. Our simulations also revealed interesting dynamical properties not previously predicted. The model predicts that the hrp regulon is a biologically stable two-state system, with each of the stable states being strongly attractive, a feature indicative of selection for a tightly regulated and responsive system. The model predicts that the state of the GacS/GacA node confers control, a prediction that is consistent with experimental observations that the protein has a role as master regulator. Simulated gene "knock out" experiments with the model predict that HrpL is a central information processing point within the network.

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States of the model at step 10 in runs with simulated knock-outs of individual genes.We ran the model in synchronous mode for 10 steps from the initial state and simulated a knock-out of a single gene, recording the model's state at step 10. Each column represents the results from a single run with a single knocked out gene, indicated above the column, each row represents a gene. Blue colour indicates that the model showed the gene was in the ‘True’ state at step 10, no colour indicates the model showed ‘False’ for the protein at step 10.
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pone-0009101-g004: States of the model at step 10 in runs with simulated knock-outs of individual genes.We ran the model in synchronous mode for 10 steps from the initial state and simulated a knock-out of a single gene, recording the model's state at step 10. Each column represents the results from a single run with a single knocked out gene, indicated above the column, each row represents a gene. Blue colour indicates that the model showed the gene was in the ‘True’ state at step 10, no colour indicates the model showed ‘False’ for the protein at step 10.

Mentions: To find essential nodes in the model it is possible so to determine whether any proteins in the model could be essential to the normal steady state that we have already described above and in Figure 1 we performed synthetic knock-outs, running the model synchronously with a single protein's state set to False throughout the run. Figure 4 shows the results of this analysis. In Figure 4 each of the columns represents the tenth state for a run with the protein at the head of the column knocked-out. A blue cell indicates that the protein on the row was in the True state, a white cell indicates that the protein was in the False state. Absence of GacSGacA leaves the model in the ‘off’ attractor state. The analysis reveals proteins HrpRS, RpoN and HrpL are all required for the expression of the other hrp proteins in the model, but are not dependent on them for their own expression. As HrpL is directly downstream of both HrpRS and RpoN and only activates state when both inputs are True it can be considered that HrpL functions as an integrator of these two inputs, requiring that both are received for the activation of the rest of the regulon. It could be argued that the pathogen would easily be able to circumvent these switches by over expressing a single component, such as HrpL, but this would result in constitutively expressing the TTSS and would likely to be disadvantageous.


A Boolean model of the Pseudomonas syringae hrp regulon predicts a tightly regulated system.

MacLean D, Studholme DJ - PLoS ONE (2010)

States of the model at step 10 in runs with simulated knock-outs of individual genes.We ran the model in synchronous mode for 10 steps from the initial state and simulated a knock-out of a single gene, recording the model's state at step 10. Each column represents the results from a single run with a single knocked out gene, indicated above the column, each row represents a gene. Blue colour indicates that the model showed the gene was in the ‘True’ state at step 10, no colour indicates the model showed ‘False’ for the protein at step 10.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2821412&req=5

pone-0009101-g004: States of the model at step 10 in runs with simulated knock-outs of individual genes.We ran the model in synchronous mode for 10 steps from the initial state and simulated a knock-out of a single gene, recording the model's state at step 10. Each column represents the results from a single run with a single knocked out gene, indicated above the column, each row represents a gene. Blue colour indicates that the model showed the gene was in the ‘True’ state at step 10, no colour indicates the model showed ‘False’ for the protein at step 10.
Mentions: To find essential nodes in the model it is possible so to determine whether any proteins in the model could be essential to the normal steady state that we have already described above and in Figure 1 we performed synthetic knock-outs, running the model synchronously with a single protein's state set to False throughout the run. Figure 4 shows the results of this analysis. In Figure 4 each of the columns represents the tenth state for a run with the protein at the head of the column knocked-out. A blue cell indicates that the protein on the row was in the True state, a white cell indicates that the protein was in the False state. Absence of GacSGacA leaves the model in the ‘off’ attractor state. The analysis reveals proteins HrpRS, RpoN and HrpL are all required for the expression of the other hrp proteins in the model, but are not dependent on them for their own expression. As HrpL is directly downstream of both HrpRS and RpoN and only activates state when both inputs are True it can be considered that HrpL functions as an integrator of these two inputs, requiring that both are received for the activation of the rest of the regulon. It could be argued that the pathogen would easily be able to circumvent these switches by over expressing a single component, such as HrpL, but this would result in constitutively expressing the TTSS and would likely to be disadvantageous.

Bottom Line: The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease.Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole.We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected.

View Article: PubMed Central - PubMed

Affiliation: The Sainsbury Laboratory, John Innes Centre, Norwich, United Kingdom. dan.maclean@tsl.ac.uk

ABSTRACT
The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease. Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole. In order to gain a better understanding of these dynamical behaviours and to create a basis for the refinement of the experimentally derived knowledge we created a Boolean model of the regulatory interactions within the hrp regulon of Pseudomonas syringae pathovar tomato strain DC3000 Pseudomonas syringae. We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected. Our simulations also revealed interesting dynamical properties not previously predicted. The model predicts that the hrp regulon is a biologically stable two-state system, with each of the stable states being strongly attractive, a feature indicative of selection for a tightly regulated and responsive system. The model predicts that the state of the GacS/GacA node confers control, a prediction that is consistent with experimental observations that the protein has a role as master regulator. Simulated gene "knock out" experiments with the model predict that HrpL is a central information processing point within the network.

Show MeSH
Related in: MedlinePlus