Limits...
Determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation.

Birch EW, Ruggero NA, Covert MW - PLoS Comput. Biol. (2012)

Bottom Line: The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production.For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict.Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

View Article: PubMed Central - PubMed

Affiliation: Chemical Engineering, Stanford University, Stanford, CA, USA.

ABSTRACT
Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

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Format and method for the integrated simulation.(A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the  matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.
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pcbi-1002746-g002: Format and method for the integrated simulation.(A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.

Mentions: Consequently, we devised a metabolite allocation-based approach to bounding reaction rates. Recognizing that the host-viral metabolic interface is the set of common metabolites used in macromolecule synthesis, we split the matrix formulation (Figure 2A) into a sum of metabolite rate vectors that represent the host supply and viral demand, where the former constrains the latter. Given a selected host flux distribution, we calculate a strict bound on viral metabolite use. Due to the lack of kinetic information about how the viral metabolic reactions contribute to the metabolite demand, we assume that all viral reactions have an equal and high affinity for precursor metabolites. After calculating rates for the viral reactions from the T7 ODEs to determine the demand for viral metabolites, we scale the rates of all reactions consuming a given metabolite by the same fraction such that total demand is brought within host supply. This method assures that while all reactions are limited evenly, no reaction is limited by a metabolite it does not consume; if amino acids are scarce but dNTPs are available, genome synthesis can proceed but translation cannot.


Determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation.

Birch EW, Ruggero NA, Covert MW - PLoS Comput. Biol. (2012)

Format and method for the integrated simulation.(A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the  matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3475664&req=5

pcbi-1002746-g002: Format and method for the integrated simulation.(A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.
Mentions: Consequently, we devised a metabolite allocation-based approach to bounding reaction rates. Recognizing that the host-viral metabolic interface is the set of common metabolites used in macromolecule synthesis, we split the matrix formulation (Figure 2A) into a sum of metabolite rate vectors that represent the host supply and viral demand, where the former constrains the latter. Given a selected host flux distribution, we calculate a strict bound on viral metabolite use. Due to the lack of kinetic information about how the viral metabolic reactions contribute to the metabolite demand, we assume that all viral reactions have an equal and high affinity for precursor metabolites. After calculating rates for the viral reactions from the T7 ODEs to determine the demand for viral metabolites, we scale the rates of all reactions consuming a given metabolite by the same fraction such that total demand is brought within host supply. This method assures that while all reactions are limited evenly, no reaction is limited by a metabolite it does not consume; if amino acids are scarce but dNTPs are available, genome synthesis can proceed but translation cannot.

Bottom Line: The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production.For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict.Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

View Article: PubMed Central - PubMed

Affiliation: Chemical Engineering, Stanford University, Stanford, CA, USA.

ABSTRACT
Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

Show MeSH
Related in: MedlinePlus