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Bridging the gap between gene expression and metabolic phenotype via kinetic models.

Vital-Lopez FG, Wallqvist A, Reifman J - BMC Syst Biol (2013)

Bottom Line: We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes.In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism.

View Article: PubMed Central - HTML - PubMed

Affiliation: DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advance Technology Research Center, U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD 21702, USA.

ABSTRACT

Background: Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.

Results: We developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.

Conclusions: Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

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Contribution of gene expression changes of individual reactions to weak organic acid (WOA) treatment tolerance. (A) Definition of the metric used to compare the effects of gene expression changes of individual reactions. The normalized uptake change (NUC) for reaction i is defined as the average of the changes in the WOA uptake rate (Δwur+i and Δwur-i) that reduced biomass to 5.0% of the reference value in simulations where only the gene expression data (GED) of reaction i (gi) are considered (No GED+gi) or excluded (GED-gi). The average was normalized by the difference in the WOA uptake rate that reduced the biomass to 5.0% of the reference value in simulations with (GED) and without (No GED) gene expression changes for all reactions. (B) Normalized uptake changes for each WOA. We only show the reactions with 10 higher contributions. EC denotes the Enzyme Commission number. The y-axis shows only the EC number for the first step of lumped reactions (L). Note that the two most influential reactions, in bold font, were EC 2.7.1.1 and EC 4.1.1.1, for all cases.
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Figure 8: Contribution of gene expression changes of individual reactions to weak organic acid (WOA) treatment tolerance. (A) Definition of the metric used to compare the effects of gene expression changes of individual reactions. The normalized uptake change (NUC) for reaction i is defined as the average of the changes in the WOA uptake rate (Δwur+i and Δwur-i) that reduced biomass to 5.0% of the reference value in simulations where only the gene expression data (GED) of reaction i (gi) are considered (No GED+gi) or excluded (GED-gi). The average was normalized by the difference in the WOA uptake rate that reduced the biomass to 5.0% of the reference value in simulations with (GED) and without (No GED) gene expression changes for all reactions. (B) Normalized uptake changes for each WOA. We only show the reactions with 10 higher contributions. EC denotes the Enzyme Commission number. The y-axis shows only the EC number for the first step of lumped reactions (L). Note that the two most influential reactions, in bold font, were EC 2.7.1.1 and EC 4.1.1.1, for all cases.

Mentions: As in the simulations under histidine starvation, we used the models to determine the effect of gene expression changes associated with individual reactions on the ability of S. cerevisiae to grow at the dilution rate under WOA treatment. Here, we used the predicted WOA uptake rate that decreases the biomass concentration to 5.0% of the biomass of the untreated culture as a measure of tolerance as illustrated in Figure 8A. Figure 8B shows the changes in the tolerated WOA uptake rate resulting from gene expression changes associated with individual reactions. For all treatment conditions, the two most influential gene expression changes were those associated with uptake and phosphorylation of glucose (Enzyme Commission (EC) 2.7.1.1) and the decarboxylation of pyruvate to acetaldehyde (EC 4.1.1.1). Moreover, most of the increase in tolerance to the WOAs could be attributed to these two reactions, in contrast to the results under histidine starvation where the response was distributed among multiple reactions. Notably, the overall gene expression changes of these reactions were of relatively small magnitude in half of the cases (Table 5). To put this in perspective, when the reactions with gene associations (96 out of 125 reactions in the model) were sorted by descending order of magnitude of their overall gene expression changes, reaction EC 4.1.1.1 ranked below 10 in three out of the four cases. Note that we did not include in the analysis the gene expression changes associated with the efflux processes of protons and carboxylic anions [31]. Besides the two most influential reactions, only a few others had a significant individual contribution to the predicted tolerance. This result is in agreement with the “sloppiness” property, in the sense that a network’s function (growth under WOA treatment in this case) is determined by a reduced number of parameters (in this case, gene expression changes of a few reactions).


Bridging the gap between gene expression and metabolic phenotype via kinetic models.

Vital-Lopez FG, Wallqvist A, Reifman J - BMC Syst Biol (2013)

Contribution of gene expression changes of individual reactions to weak organic acid (WOA) treatment tolerance. (A) Definition of the metric used to compare the effects of gene expression changes of individual reactions. The normalized uptake change (NUC) for reaction i is defined as the average of the changes in the WOA uptake rate (Δwur+i and Δwur-i) that reduced biomass to 5.0% of the reference value in simulations where only the gene expression data (GED) of reaction i (gi) are considered (No GED+gi) or excluded (GED-gi). The average was normalized by the difference in the WOA uptake rate that reduced the biomass to 5.0% of the reference value in simulations with (GED) and without (No GED) gene expression changes for all reactions. (B) Normalized uptake changes for each WOA. We only show the reactions with 10 higher contributions. EC denotes the Enzyme Commission number. The y-axis shows only the EC number for the first step of lumped reactions (L). Note that the two most influential reactions, in bold font, were EC 2.7.1.1 and EC 4.1.1.1, for all cases.
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Figure 8: Contribution of gene expression changes of individual reactions to weak organic acid (WOA) treatment tolerance. (A) Definition of the metric used to compare the effects of gene expression changes of individual reactions. The normalized uptake change (NUC) for reaction i is defined as the average of the changes in the WOA uptake rate (Δwur+i and Δwur-i) that reduced biomass to 5.0% of the reference value in simulations where only the gene expression data (GED) of reaction i (gi) are considered (No GED+gi) or excluded (GED-gi). The average was normalized by the difference in the WOA uptake rate that reduced the biomass to 5.0% of the reference value in simulations with (GED) and without (No GED) gene expression changes for all reactions. (B) Normalized uptake changes for each WOA. We only show the reactions with 10 higher contributions. EC denotes the Enzyme Commission number. The y-axis shows only the EC number for the first step of lumped reactions (L). Note that the two most influential reactions, in bold font, were EC 2.7.1.1 and EC 4.1.1.1, for all cases.
Mentions: As in the simulations under histidine starvation, we used the models to determine the effect of gene expression changes associated with individual reactions on the ability of S. cerevisiae to grow at the dilution rate under WOA treatment. Here, we used the predicted WOA uptake rate that decreases the biomass concentration to 5.0% of the biomass of the untreated culture as a measure of tolerance as illustrated in Figure 8A. Figure 8B shows the changes in the tolerated WOA uptake rate resulting from gene expression changes associated with individual reactions. For all treatment conditions, the two most influential gene expression changes were those associated with uptake and phosphorylation of glucose (Enzyme Commission (EC) 2.7.1.1) and the decarboxylation of pyruvate to acetaldehyde (EC 4.1.1.1). Moreover, most of the increase in tolerance to the WOAs could be attributed to these two reactions, in contrast to the results under histidine starvation where the response was distributed among multiple reactions. Notably, the overall gene expression changes of these reactions were of relatively small magnitude in half of the cases (Table 5). To put this in perspective, when the reactions with gene associations (96 out of 125 reactions in the model) were sorted by descending order of magnitude of their overall gene expression changes, reaction EC 4.1.1.1 ranked below 10 in three out of the four cases. Note that we did not include in the analysis the gene expression changes associated with the efflux processes of protons and carboxylic anions [31]. Besides the two most influential reactions, only a few others had a significant individual contribution to the predicted tolerance. This result is in agreement with the “sloppiness” property, in the sense that a network’s function (growth under WOA treatment in this case) is determined by a reduced number of parameters (in this case, gene expression changes of a few reactions).

Bottom Line: We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes.In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism.

View Article: PubMed Central - HTML - PubMed

Affiliation: DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advance Technology Research Center, U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD 21702, USA.

ABSTRACT

Background: Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.

Results: We developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.

Conclusions: Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

Show MeSH