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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Infected host fluxes on tryptone media.(A) Flux dynamics are displayed for a subset of the metabolic network map. Arrows representing reactions and the subplots of flux through those reactions are colored according to clustering of flux dynamics. Positive flux values correspond to the reaction direction indicated by the colored arrowhead, negative flux direction is depicted with light grey barbs. Asterisks (*) represent an abbreviation of the arrow for uptake from media. Metabolite abbreviations are consistent with FBA model definition. For clustering, fluxes were treated as vectors with (1-correlation) as distance, and clustered using average hierarchical grouping with a cutoff height of 0.25. Clusters with fewer than ten members appear in black, and clusters with constant dynamics are highlighted in grey. All nonzero fluxes in any media (tryptone, glucose, succinate, and acetate) were included in the flux clustering so that cluster designation and color coding is consistent across media and figures. Maps for media other than tryptone are included in the SI. (B) Select flux dynamics expanded for clarity ordered to exemplify host flux changes driven by viral dynamics: (i) host amino acid synthesis, (ii) major viral capsid protein synthesis, (iii) host nucleotide phosphorylation, (iv) viral digestion of host genome to dNMPs, (v) purine biosynthesis, (vi) viral mRNA synthesis, (vii) viral genome synthesis, (viii) host cell envelope biosynthesis, (ix) host biomass accumulation.
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pcbi-1002746-g004: Infected host fluxes on tryptone media.(A) Flux dynamics are displayed for a subset of the metabolic network map. Arrows representing reactions and the subplots of flux through those reactions are colored according to clustering of flux dynamics. Positive flux values correspond to the reaction direction indicated by the colored arrowhead, negative flux direction is depicted with light grey barbs. Asterisks (*) represent an abbreviation of the arrow for uptake from media. Metabolite abbreviations are consistent with FBA model definition. For clustering, fluxes were treated as vectors with (1-correlation) as distance, and clustered using average hierarchical grouping with a cutoff height of 0.25. Clusters with fewer than ten members appear in black, and clusters with constant dynamics are highlighted in grey. All nonzero fluxes in any media (tryptone, glucose, succinate, and acetate) were included in the flux clustering so that cluster designation and color coding is consistent across media and figures. Maps for media other than tryptone are included in the SI. (B) Select flux dynamics expanded for clarity ordered to exemplify host flux changes driven by viral dynamics: (i) host amino acid synthesis, (ii) major viral capsid protein synthesis, (iii) host nucleotide phosphorylation, (iv) viral digestion of host genome to dNMPs, (v) purine biosynthesis, (vi) viral mRNA synthesis, (vii) viral genome synthesis, (viii) host cell envelope biosynthesis, (ix) host biomass accumulation.

Mentions: After considering the phage reaction changes in the integrated simulation, we used the model to investigate the changes in host metabolism during infection. The flux-balance component of the integrated model calculates a predicted flux distribution for E. coli growth on tryptone in the presence and absence of phage. Essentially all of the non-zero fluxes change dynamically over time in the presence of T7; a subset of these changes are shown alongside the underlying metabolic map (Figure 4). Many metabolic reactions experienced prominent flux changes that were coordinated during infection. Dynamic coordination of fluxes in time is not particularly surprising considering the underlying network structure of constraints. However, these similarities in addition to the sheer number of total fluxes that require consideration render unaided visual inspection of infection dynamic information rather uninformative. We found it useful to cluster the flux dynamics into broad categories, which facilitate interpretation of the interesting flux patterns in central and peripheral metabolism during viral replication.


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

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

Infected host fluxes on tryptone media.(A) Flux dynamics are displayed for a subset of the metabolic network map. Arrows representing reactions and the subplots of flux through those reactions are colored according to clustering of flux dynamics. Positive flux values correspond to the reaction direction indicated by the colored arrowhead, negative flux direction is depicted with light grey barbs. Asterisks (*) represent an abbreviation of the arrow for uptake from media. Metabolite abbreviations are consistent with FBA model definition. For clustering, fluxes were treated as vectors with (1-correlation) as distance, and clustered using average hierarchical grouping with a cutoff height of 0.25. Clusters with fewer than ten members appear in black, and clusters with constant dynamics are highlighted in grey. All nonzero fluxes in any media (tryptone, glucose, succinate, and acetate) were included in the flux clustering so that cluster designation and color coding is consistent across media and figures. Maps for media other than tryptone are included in the SI. (B) Select flux dynamics expanded for clarity ordered to exemplify host flux changes driven by viral dynamics: (i) host amino acid synthesis, (ii) major viral capsid protein synthesis, (iii) host nucleotide phosphorylation, (iv) viral digestion of host genome to dNMPs, (v) purine biosynthesis, (vi) viral mRNA synthesis, (vii) viral genome synthesis, (viii) host cell envelope biosynthesis, (ix) host biomass accumulation.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3475664&req=5

pcbi-1002746-g004: Infected host fluxes on tryptone media.(A) Flux dynamics are displayed for a subset of the metabolic network map. Arrows representing reactions and the subplots of flux through those reactions are colored according to clustering of flux dynamics. Positive flux values correspond to the reaction direction indicated by the colored arrowhead, negative flux direction is depicted with light grey barbs. Asterisks (*) represent an abbreviation of the arrow for uptake from media. Metabolite abbreviations are consistent with FBA model definition. For clustering, fluxes were treated as vectors with (1-correlation) as distance, and clustered using average hierarchical grouping with a cutoff height of 0.25. Clusters with fewer than ten members appear in black, and clusters with constant dynamics are highlighted in grey. All nonzero fluxes in any media (tryptone, glucose, succinate, and acetate) were included in the flux clustering so that cluster designation and color coding is consistent across media and figures. Maps for media other than tryptone are included in the SI. (B) Select flux dynamics expanded for clarity ordered to exemplify host flux changes driven by viral dynamics: (i) host amino acid synthesis, (ii) major viral capsid protein synthesis, (iii) host nucleotide phosphorylation, (iv) viral digestion of host genome to dNMPs, (v) purine biosynthesis, (vi) viral mRNA synthesis, (vii) viral genome synthesis, (viii) host cell envelope biosynthesis, (ix) host biomass accumulation.
Mentions: After considering the phage reaction changes in the integrated simulation, we used the model to investigate the changes in host metabolism during infection. The flux-balance component of the integrated model calculates a predicted flux distribution for E. coli growth on tryptone in the presence and absence of phage. Essentially all of the non-zero fluxes change dynamically over time in the presence of T7; a subset of these changes are shown alongside the underlying metabolic map (Figure 4). Many metabolic reactions experienced prominent flux changes that were coordinated during infection. Dynamic coordination of fluxes in time is not particularly surprising considering the underlying network structure of constraints. However, these similarities in addition to the sheer number of total fluxes that require consideration render unaided visual inspection of infection dynamic information rather uninformative. We found it useful to cluster the flux dynamics into broad categories, which facilitate interpretation of the interesting flux patterns in central and peripheral metabolism during viral replication.

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