Limits...
Inferring carbon sources from gene expression profiles using metabolic flux models.

Brandes A, Lun DS, Ip K, Zucker J, Colijn C, Weiner B, Galagan JE - PLoS ONE (2012)

Bottom Line: Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level.Inferences about a microorganism's nutrient environment can be made by integrating gene expression data into a metabolic framework.This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism's alteration of gene expression is adaptive to its nutrient environment.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

ABSTRACT

Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism's metabolic network.

Principal findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not.

Conclusions: Inferences about a microorganism's nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism's alteration of gene expression is adaptive to its nutrient environment.

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The principles of our method illustrated using a simple metabolic network.Flux limits in this figure are represented by a thin black outline. Reaction fluxes are represented as shaded regions with flux magnitude proportional to thickness. Flux direction is not indicated. Panel A. Creation of the baseline flux limits (corresponding to  in Figure 2, Panel A). Each reaction is given a flux limit corresponding to the maximum optimal flux solution over the two in silico nutrient uptake conditions. The shading is orange for in silico glucose growth, blue for in silico acetate and grey where the two solutions overlap. Panel B. Creation of the glucose expression-derived flux limits (corresponding to  in Figure 2, Panel B). Each flux limit shown in Panel A has been scaled by the level of gene expression for in vivo growth on glucose relative to the maximum gene expression for that reaction over both nutrient conditions. The arrows indicate two reactions for which gene expression was significantly lower on glucose than on acetate, resulting in significantly reduced flux limits. Panel C. Effect of the glucose expression-derived flux limits of Panel B on in silico glucose growth. The glucose optimal flux from Panel A (orange region) lies within the limits; biomass production is not changed. Panel D. Effect of the glucose expression-derived flux limits of Panel B on in silico acetate growth. The acetate optimal flux from Panel A (blue region) exceeds the flux limits for several reactions. (This is analogous to the optimal flux vector  lying outside the flux cone in Figure 2, Panel B.) Hence the flux limits will lead to smaller optimal fluxes for these reactions and reduced biomass production. Relative biomass production is therefore smaller for in silico acetate than for in silico glucose, and we conclude that glucose is the more likely carbon source for the expression data.
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pone-0036947-g001: The principles of our method illustrated using a simple metabolic network.Flux limits in this figure are represented by a thin black outline. Reaction fluxes are represented as shaded regions with flux magnitude proportional to thickness. Flux direction is not indicated. Panel A. Creation of the baseline flux limits (corresponding to in Figure 2, Panel A). Each reaction is given a flux limit corresponding to the maximum optimal flux solution over the two in silico nutrient uptake conditions. The shading is orange for in silico glucose growth, blue for in silico acetate and grey where the two solutions overlap. Panel B. Creation of the glucose expression-derived flux limits (corresponding to in Figure 2, Panel B). Each flux limit shown in Panel A has been scaled by the level of gene expression for in vivo growth on glucose relative to the maximum gene expression for that reaction over both nutrient conditions. The arrows indicate two reactions for which gene expression was significantly lower on glucose than on acetate, resulting in significantly reduced flux limits. Panel C. Effect of the glucose expression-derived flux limits of Panel B on in silico glucose growth. The glucose optimal flux from Panel A (orange region) lies within the limits; biomass production is not changed. Panel D. Effect of the glucose expression-derived flux limits of Panel B on in silico acetate growth. The acetate optimal flux from Panel A (blue region) exceeds the flux limits for several reactions. (This is analogous to the optimal flux vector lying outside the flux cone in Figure 2, Panel B.) Hence the flux limits will lead to smaller optimal fluxes for these reactions and reduced biomass production. Relative biomass production is therefore smaller for in silico acetate than for in silico glucose, and we conclude that glucose is the more likely carbon source for the expression data.

Mentions: First, create a set of baseline flux limits for the metabolic model. Our procedure is intended to estimate the maximal flux capacity of each reaction for simulated growth over the selected set of in silico candidate nutrients (Figure 1, Panel A). This requires setting a realistic in vitro growth rate and computing the corresponding in silico nutrient supply rate.


Inferring carbon sources from gene expression profiles using metabolic flux models.

Brandes A, Lun DS, Ip K, Zucker J, Colijn C, Weiner B, Galagan JE - PLoS ONE (2012)

The principles of our method illustrated using a simple metabolic network.Flux limits in this figure are represented by a thin black outline. Reaction fluxes are represented as shaded regions with flux magnitude proportional to thickness. Flux direction is not indicated. Panel A. Creation of the baseline flux limits (corresponding to  in Figure 2, Panel A). Each reaction is given a flux limit corresponding to the maximum optimal flux solution over the two in silico nutrient uptake conditions. The shading is orange for in silico glucose growth, blue for in silico acetate and grey where the two solutions overlap. Panel B. Creation of the glucose expression-derived flux limits (corresponding to  in Figure 2, Panel B). Each flux limit shown in Panel A has been scaled by the level of gene expression for in vivo growth on glucose relative to the maximum gene expression for that reaction over both nutrient conditions. The arrows indicate two reactions for which gene expression was significantly lower on glucose than on acetate, resulting in significantly reduced flux limits. Panel C. Effect of the glucose expression-derived flux limits of Panel B on in silico glucose growth. The glucose optimal flux from Panel A (orange region) lies within the limits; biomass production is not changed. Panel D. Effect of the glucose expression-derived flux limits of Panel B on in silico acetate growth. The acetate optimal flux from Panel A (blue region) exceeds the flux limits for several reactions. (This is analogous to the optimal flux vector  lying outside the flux cone in Figure 2, Panel B.) Hence the flux limits will lead to smaller optimal fluxes for these reactions and reduced biomass production. Relative biomass production is therefore smaller for in silico acetate than for in silico glucose, and we conclude that glucose is the more likely carbon source for the expression data.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3351459&req=5

pone-0036947-g001: The principles of our method illustrated using a simple metabolic network.Flux limits in this figure are represented by a thin black outline. Reaction fluxes are represented as shaded regions with flux magnitude proportional to thickness. Flux direction is not indicated. Panel A. Creation of the baseline flux limits (corresponding to in Figure 2, Panel A). Each reaction is given a flux limit corresponding to the maximum optimal flux solution over the two in silico nutrient uptake conditions. The shading is orange for in silico glucose growth, blue for in silico acetate and grey where the two solutions overlap. Panel B. Creation of the glucose expression-derived flux limits (corresponding to in Figure 2, Panel B). Each flux limit shown in Panel A has been scaled by the level of gene expression for in vivo growth on glucose relative to the maximum gene expression for that reaction over both nutrient conditions. The arrows indicate two reactions for which gene expression was significantly lower on glucose than on acetate, resulting in significantly reduced flux limits. Panel C. Effect of the glucose expression-derived flux limits of Panel B on in silico glucose growth. The glucose optimal flux from Panel A (orange region) lies within the limits; biomass production is not changed. Panel D. Effect of the glucose expression-derived flux limits of Panel B on in silico acetate growth. The acetate optimal flux from Panel A (blue region) exceeds the flux limits for several reactions. (This is analogous to the optimal flux vector lying outside the flux cone in Figure 2, Panel B.) Hence the flux limits will lead to smaller optimal fluxes for these reactions and reduced biomass production. Relative biomass production is therefore smaller for in silico acetate than for in silico glucose, and we conclude that glucose is the more likely carbon source for the expression data.
Mentions: First, create a set of baseline flux limits for the metabolic model. Our procedure is intended to estimate the maximal flux capacity of each reaction for simulated growth over the selected set of in silico candidate nutrients (Figure 1, Panel A). This requires setting a realistic in vitro growth rate and computing the corresponding in silico nutrient supply rate.

Bottom Line: Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level.Inferences about a microorganism's nutrient environment can be made by integrating gene expression data into a metabolic framework.This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism's alteration of gene expression is adaptive to its nutrient environment.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

ABSTRACT

Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism's metabolic network.

Principal findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not.

Conclusions: Inferences about a microorganism's nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism's alteration of gene expression is adaptive to its nutrient environment.

Show MeSH
Related in: MedlinePlus