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 with flux cones.Only three of the many reaction fluxes are shown. For simplicity only two in silico candidate nutrients are represented. The figure does not correspond to actual experimental data. Panel A. Creation of the baseline flux limits, represented as a rectangular parallelpiped. Reaction fluxes must lie within the flux cone (grey area). Flux vectors producing maximal biomass for candidate nutrient i are indicated by colored asterisks and labeled  These solutions of the baseline FBA model constrained by in silico nutrient uptake lie on the surface of the flux cone. For each dimension j the baseline upper flux limit is denoted Panel B. Creation of the expression-derived flux limits by scaling the baseline flux limits. The upper flux limit for dimension j derived using expression data for the unknown in vitro nutrient l is denoted  and the solution vectors are denoted  The baseline flux limits are indicated with dashed lines, the scaled limits are indicated by solid lines. In this hypothetical example the expression of the gene for the enzymatic reaction producing flux v2 is 40% of the maximal expression level for that gene under the other nutrient condition. The maximal flux for this reaction is set to 40% of its original level. This smaller flux cone represents the metabolic capabilities of the organism under the corresponding growth condition. The solution vector producing optimal biomass for nutrient 1 has not changed with the new flux limits, but the solution vector for nutrient 2 has been reduced in magnitude, with a consequent reduction in biomass production. Relative biomass production will be larger for nutrient 1 than for nutrient 2. We would therefore conclude that the in vitro nutrient l that gave rise to the expression profile is probably nutrient 1, rather than nutrient 2.
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pone-0036947-g002: The principles of our method illustrated with flux cones.Only three of the many reaction fluxes are shown. For simplicity only two in silico candidate nutrients are represented. The figure does not correspond to actual experimental data. Panel A. Creation of the baseline flux limits, represented as a rectangular parallelpiped. Reaction fluxes must lie within the flux cone (grey area). Flux vectors producing maximal biomass for candidate nutrient i are indicated by colored asterisks and labeled These solutions of the baseline FBA model constrained by in silico nutrient uptake lie on the surface of the flux cone. For each dimension j the baseline upper flux limit is denoted Panel B. Creation of the expression-derived flux limits by scaling the baseline flux limits. The upper flux limit for dimension j derived using expression data for the unknown in vitro nutrient l is denoted and the solution vectors are denoted The baseline flux limits are indicated with dashed lines, the scaled limits are indicated by solid lines. In this hypothetical example the expression of the gene for the enzymatic reaction producing flux v2 is 40% of the maximal expression level for that gene under the other nutrient condition. The maximal flux for this reaction is set to 40% of its original level. This smaller flux cone represents the metabolic capabilities of the organism under the corresponding growth condition. The solution vector producing optimal biomass for nutrient 1 has not changed with the new flux limits, but the solution vector for nutrient 2 has been reduced in magnitude, with a consequent reduction in biomass production. Relative biomass production will be larger for nutrient 1 than for nutrient 2. We would therefore conclude that the in vitro nutrient l that gave rise to the expression profile is probably nutrient 1, rather than nutrient 2.

Mentions: Next, create expression-derived flux limits. These limits are specific to a challenge gene expression set, and are computed by scaling the baseline flux limits using the ratio of each gene’s expression level in the challenge condition to its maximum over several gene expression sets (see Figure 1, Panel B, and Figure 2, Panel B). The resulting flux limits reflect the organism’s adaptation to the unknown in vitro nutrient condition


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 with flux cones.Only three of the many reaction fluxes are shown. For simplicity only two in silico candidate nutrients are represented. The figure does not correspond to actual experimental data. Panel A. Creation of the baseline flux limits, represented as a rectangular parallelpiped. Reaction fluxes must lie within the flux cone (grey area). Flux vectors producing maximal biomass for candidate nutrient i are indicated by colored asterisks and labeled  These solutions of the baseline FBA model constrained by in silico nutrient uptake lie on the surface of the flux cone. For each dimension j the baseline upper flux limit is denoted Panel B. Creation of the expression-derived flux limits by scaling the baseline flux limits. The upper flux limit for dimension j derived using expression data for the unknown in vitro nutrient l is denoted  and the solution vectors are denoted  The baseline flux limits are indicated with dashed lines, the scaled limits are indicated by solid lines. In this hypothetical example the expression of the gene for the enzymatic reaction producing flux v2 is 40% of the maximal expression level for that gene under the other nutrient condition. The maximal flux for this reaction is set to 40% of its original level. This smaller flux cone represents the metabolic capabilities of the organism under the corresponding growth condition. The solution vector producing optimal biomass for nutrient 1 has not changed with the new flux limits, but the solution vector for nutrient 2 has been reduced in magnitude, with a consequent reduction in biomass production. Relative biomass production will be larger for nutrient 1 than for nutrient 2. We would therefore conclude that the in vitro nutrient l that gave rise to the expression profile is probably nutrient 1, rather than nutrient 2.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3351459&req=5

pone-0036947-g002: The principles of our method illustrated with flux cones.Only three of the many reaction fluxes are shown. For simplicity only two in silico candidate nutrients are represented. The figure does not correspond to actual experimental data. Panel A. Creation of the baseline flux limits, represented as a rectangular parallelpiped. Reaction fluxes must lie within the flux cone (grey area). Flux vectors producing maximal biomass for candidate nutrient i are indicated by colored asterisks and labeled These solutions of the baseline FBA model constrained by in silico nutrient uptake lie on the surface of the flux cone. For each dimension j the baseline upper flux limit is denoted Panel B. Creation of the expression-derived flux limits by scaling the baseline flux limits. The upper flux limit for dimension j derived using expression data for the unknown in vitro nutrient l is denoted and the solution vectors are denoted The baseline flux limits are indicated with dashed lines, the scaled limits are indicated by solid lines. In this hypothetical example the expression of the gene for the enzymatic reaction producing flux v2 is 40% of the maximal expression level for that gene under the other nutrient condition. The maximal flux for this reaction is set to 40% of its original level. This smaller flux cone represents the metabolic capabilities of the organism under the corresponding growth condition. The solution vector producing optimal biomass for nutrient 1 has not changed with the new flux limits, but the solution vector for nutrient 2 has been reduced in magnitude, with a consequent reduction in biomass production. Relative biomass production will be larger for nutrient 1 than for nutrient 2. We would therefore conclude that the in vitro nutrient l that gave rise to the expression profile is probably nutrient 1, rather than nutrient 2.
Mentions: Next, create expression-derived flux limits. These limits are specific to a challenge gene expression set, and are computed by scaling the baseline flux limits using the ratio of each gene’s expression level in the challenge condition to its maximum over several gene expression sets (see Figure 1, Panel B, and Figure 2, Panel B). The resulting flux limits reflect the organism’s adaptation to the unknown in vitro nutrient condition

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