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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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Biomass concentration as a function of the weak organic acid (WOA) uptake rate. The curves were constructed by using the model to simulate increasing WOA uptake rates using the experimental gene expression data (GED), assuming no gene expression changes (No GED), and by extrapolating the gene expression changes (i.e., gene expression ratios on a logarithmic scale were multiplied by 2.0). The uptake rate and biomass concentration were normalized using the uptake rate of acetic acid and biomass concentration under the reference condition, respectively. Vertical dashed lines indicate the WOA uptake rate under the treatment conditions.
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Figure 7: Biomass concentration as a function of the weak organic acid (WOA) uptake rate. The curves were constructed by using the model to simulate increasing WOA uptake rates using the experimental gene expression data (GED), assuming no gene expression changes (No GED), and by extrapolating the gene expression changes (i.e., gene expression ratios on a logarithmic scale were multiplied by 2.0). The uptake rate and biomass concentration were normalized using the uptake rate of acetic acid and biomass concentration under the reference condition, respectively. Vertical dashed lines indicate the WOA uptake rate under the treatment conditions.

Mentions: We also used the constructed models to investigate the role of the transcriptional response on the tolerance of S. cerevisiae to WOA treatment (i.e., its ability to grow at the dilution rate of the chemostat under WOA exposure). In principle, S. cerevisiae should adjust its gene expression levels to better cope with these stress conditions. To probe if model predictions were in line with this premise, we predicted the biomass level as a function of the WOA uptake rate in treated cultures with and without gene expression changes. In addition, hypothesizing that the transcriptional response was graded depending on the stress intensity, we tested if, at higher WOA uptake rates, amplified gene expression changes would result in higher biomass growth than the measured gene expression changes. Thus, we also predicted the biomass levels assuming gene expression changes extrapolated from the experimental data (i.e., experimental gene expression ratios on a logarithmic scale multiplied by 2.0). Figure 7 shows the simulation results for each WOA. The models predicted that cultures with no gene expression changes produced the highest biomass level at the reference condition (i.e., normalized WOA uptake rate equal to 1.0). In contrast, simulations with the expression data had the highest biomass level (except for sorbic acid treatment) at the estimated uptake rate at which the expression data for the treated cultures were obtained (vertical dashed lines in Figure 7). Furthermore, the model predicted that cultures with extrapolated gene expression could tolerate higher WOA uptake rates, in agreement with the graded response assumption. Alternatively, this result suggests that we could predict the transcriptional response of S. cerevisiae to different WOA uptake rates by interpolating or extrapolating measured gene expression data. In agreement with the above premise, these simulation results suggest that the measured gene expression changes allowed S. cerevisiae to tolerate higher WOA uptake rates.


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

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

Biomass concentration as a function of the weak organic acid (WOA) uptake rate. The curves were constructed by using the model to simulate increasing WOA uptake rates using the experimental gene expression data (GED), assuming no gene expression changes (No GED), and by extrapolating the gene expression changes (i.e., gene expression ratios on a logarithmic scale were multiplied by 2.0). The uptake rate and biomass concentration were normalized using the uptake rate of acetic acid and biomass concentration under the reference condition, respectively. Vertical dashed lines indicate the WOA uptake rate under the treatment conditions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3733621&req=5

Figure 7: Biomass concentration as a function of the weak organic acid (WOA) uptake rate. The curves were constructed by using the model to simulate increasing WOA uptake rates using the experimental gene expression data (GED), assuming no gene expression changes (No GED), and by extrapolating the gene expression changes (i.e., gene expression ratios on a logarithmic scale were multiplied by 2.0). The uptake rate and biomass concentration were normalized using the uptake rate of acetic acid and biomass concentration under the reference condition, respectively. Vertical dashed lines indicate the WOA uptake rate under the treatment conditions.
Mentions: We also used the constructed models to investigate the role of the transcriptional response on the tolerance of S. cerevisiae to WOA treatment (i.e., its ability to grow at the dilution rate of the chemostat under WOA exposure). In principle, S. cerevisiae should adjust its gene expression levels to better cope with these stress conditions. To probe if model predictions were in line with this premise, we predicted the biomass level as a function of the WOA uptake rate in treated cultures with and without gene expression changes. In addition, hypothesizing that the transcriptional response was graded depending on the stress intensity, we tested if, at higher WOA uptake rates, amplified gene expression changes would result in higher biomass growth than the measured gene expression changes. Thus, we also predicted the biomass levels assuming gene expression changes extrapolated from the experimental data (i.e., experimental gene expression ratios on a logarithmic scale multiplied by 2.0). Figure 7 shows the simulation results for each WOA. The models predicted that cultures with no gene expression changes produced the highest biomass level at the reference condition (i.e., normalized WOA uptake rate equal to 1.0). In contrast, simulations with the expression data had the highest biomass level (except for sorbic acid treatment) at the estimated uptake rate at which the expression data for the treated cultures were obtained (vertical dashed lines in Figure 7). Furthermore, the model predicted that cultures with extrapolated gene expression could tolerate higher WOA uptake rates, in agreement with the graded response assumption. Alternatively, this result suggests that we could predict the transcriptional response of S. cerevisiae to different WOA uptake rates by interpolating or extrapolating measured gene expression data. In agreement with the above premise, these simulation results suggest that the measured gene expression changes allowed S. cerevisiae to tolerate higher WOA uptake rates.

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