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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Comparing normalized infected host flux dynamics spark-lines for all four media.(A) Metabolic map and normalized flux dynamics for tryptone, glucose M9, succinate M9, and acetate M9 media. Flux values were shifted to the uninfected value (), and then normalized to their maximum magnitude on each medium; zero (initial) value is indicated by a grey horizontal line. Metabolite abbreviations are consistent with FBA model definition. (B) Expansion of a selected subset of normalized fluxes. Host cell envelope synthesis (i), and biomass accumulation (ii) decrease similarity across media. Purine synthesis (iii) exhibits dynamic similarity across media. Glycolysis (iv) is observed on glucose while gluconeogenisis occurs on other media. Amino acid synthesis (v) increases on minimal media but not on amino acid-rich tryptone; and the citric acid cycle (vi) demonstrates similarity in dynamic flux change timing, but differences in scaling and direction.
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pcbi-1002746-g006: Comparing normalized infected host flux dynamics spark-lines for all four media.(A) Metabolic map and normalized flux dynamics for tryptone, glucose M9, succinate M9, and acetate M9 media. Flux values were shifted to the uninfected value (), and then normalized to their maximum magnitude on each medium; zero (initial) value is indicated by a grey horizontal line. Metabolite abbreviations are consistent with FBA model definition. (B) Expansion of a selected subset of normalized fluxes. Host cell envelope synthesis (i), and biomass accumulation (ii) decrease similarity across media. Purine synthesis (iii) exhibits dynamic similarity across media. Glycolysis (iv) is observed on glucose while gluconeogenisis occurs on other media. Amino acid synthesis (v) increases on minimal media but not on amino acid-rich tryptone; and the citric acid cycle (vi) demonstrates similarity in dynamic flux change timing, but differences in scaling and direction.

Mentions: Figure 6 displays the dynamic metabolic flux distribution for all four infection simulations, normalized to facilitate comparison. Of the fluxes that are non-zero in any of the media conditions, a large fraction show highly similar dynamics. These fluxes include critical biomass-related reactions such as those that contribute to membrane (Figure 6Bi–iii) or ribonuclotide biosynthesis. In some regions of the metabolic network, flux dynamics depend more on the media conditions; for example, in central metabolism the flux direction is often reversed between glucose and the other media because glycolysis is occurring rather than gluconeogenesis (Figure 6Biv). Reactions involved in amino acid synthesis also exhibit this phenomenon, as they increase in rate on all three minimal media, yet are zero on tryptone medium (Figure 6Bv), which contains amino acids. Another interesting example involves citric acid cycle activity, which is especially increased during the high energy demands of nucleotide recycling (Figure 6Bvi). One final subset, adjacent to key metabolites such as pyruvate (PYR), oxaloacetate (OAA), and succinate (SUCC), displayed erratic and rapid jumps between their extreme values, which results from equivalent optimal flux distributions calculated by FBA in highly interconnected sections of the metabolic network.


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

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

Comparing normalized infected host flux dynamics spark-lines for all four media.(A) Metabolic map and normalized flux dynamics for tryptone, glucose M9, succinate M9, and acetate M9 media. Flux values were shifted to the uninfected value (), and then normalized to their maximum magnitude on each medium; zero (initial) value is indicated by a grey horizontal line. Metabolite abbreviations are consistent with FBA model definition. (B) Expansion of a selected subset of normalized fluxes. Host cell envelope synthesis (i), and biomass accumulation (ii) decrease similarity across media. Purine synthesis (iii) exhibits dynamic similarity across media. Glycolysis (iv) is observed on glucose while gluconeogenisis occurs on other media. Amino acid synthesis (v) increases on minimal media but not on amino acid-rich tryptone; and the citric acid cycle (vi) demonstrates similarity in dynamic flux change timing, but differences in scaling and direction.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002746-g006: Comparing normalized infected host flux dynamics spark-lines for all four media.(A) Metabolic map and normalized flux dynamics for tryptone, glucose M9, succinate M9, and acetate M9 media. Flux values were shifted to the uninfected value (), and then normalized to their maximum magnitude on each medium; zero (initial) value is indicated by a grey horizontal line. Metabolite abbreviations are consistent with FBA model definition. (B) Expansion of a selected subset of normalized fluxes. Host cell envelope synthesis (i), and biomass accumulation (ii) decrease similarity across media. Purine synthesis (iii) exhibits dynamic similarity across media. Glycolysis (iv) is observed on glucose while gluconeogenisis occurs on other media. Amino acid synthesis (v) increases on minimal media but not on amino acid-rich tryptone; and the citric acid cycle (vi) demonstrates similarity in dynamic flux change timing, but differences in scaling and direction.
Mentions: Figure 6 displays the dynamic metabolic flux distribution for all four infection simulations, normalized to facilitate comparison. Of the fluxes that are non-zero in any of the media conditions, a large fraction show highly similar dynamics. These fluxes include critical biomass-related reactions such as those that contribute to membrane (Figure 6Bi–iii) or ribonuclotide biosynthesis. In some regions of the metabolic network, flux dynamics depend more on the media conditions; for example, in central metabolism the flux direction is often reversed between glucose and the other media because glycolysis is occurring rather than gluconeogenesis (Figure 6Biv). Reactions involved in amino acid synthesis also exhibit this phenomenon, as they increase in rate on all three minimal media, yet are zero on tryptone medium (Figure 6Bv), which contains amino acids. Another interesting example involves citric acid cycle activity, which is especially increased during the high energy demands of nucleotide recycling (Figure 6Bvi). One final subset, adjacent to key metabolites such as pyruvate (PYR), oxaloacetate (OAA), and succinate (SUCC), displayed erratic and rapid jumps between their extreme values, which results from equivalent optimal flux distributions calculated by FBA in highly interconnected sections of the metabolic network.

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