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A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress.

Achcar F, Camadro JM, Mestivier D - BMC Syst Biol (2011)

Bottom Line: The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments).Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data.All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Modelling in Integrative Biology, Institut Jacques Monod - UMR7592 - CNRS - Univ. Paris-Diderot, Paris, France.

ABSTRACT

Background: In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.

Results: Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating in silico mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all in silico mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.

Conclusions: All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

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Related in: MedlinePlus

Variations of the fluxes in selected reactions with and without oxygen. Occurrence of 10 reactions (WT model) in the simulations, compared with experimental fluxes [36] (red). A-I: frequency of the reaction at steady state (green), J: PoP of ethanol at steady state (green).
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Figure 5: Variations of the fluxes in selected reactions with and without oxygen. Occurrence of 10 reactions (WT model) in the simulations, compared with experimental fluxes [36] (red). A-I: frequency of the reaction at steady state (green), J: PoP of ethanol at steady state (green).

Mentions: We then analyzed the changes in several independent elements in the simulations of physiologically relevant transitions. One of our goals was to analyse the oxidative stress response. We therefore modeled the transition from anaerobiosis to aerobiosis. We analyzed simulations in which a key element, oxygen, was removed from the model. Oxygen is required to re-oxidise the reduced equivalents produced by cell metabolism. However, S. cerevisiae can grow in the absence of oxygen, due to its ability to shift from a respiratory to a fermentative metabolism. NADH is then oxidised by alcohol dehydrogenases, leading to ethanol production. We therefore compared the simulations of our model with different levels of oxygen to the experimental data of Jouhten et al. [36], who measured metabolic fluxes in yeast cells grown in a chemostat with a limited glucose supply in the presence of 20.9% oxygen (aerobic conditions) and under anaerobic conditions (0% oxygen). These experimental conditions were taken into account by weighting "glucose import" to a low value of 0.1. We compared the experimental fluxes with the reaction frequencies at steady state of key carbon metabolism reactions, in both the presence and absence of oxygen (Figure 5). All the in silico reactions displayed patterns of variation highly similar to those observed in vivo, with the exception of the reaction catalysed by Oac1p, a mitochondrial bidirectional oxaloacetate transporter with broad specificity for various anions. There is a simple explanation for the discrepancy observed for this specific reaction (of 10 considered). In anaerobiosis, cytoplasmic oxaloacetate is less produced than in aerobiosis. However, it is also less consumed than produced. Because the ratio of the production over the consumption is higher in anaerobiosis than in aerobiosis, oxaloacetate PoP is higher. Therefore, since one of the few remaining possible reactions involving oxaloacetate is transports by OAC1, the model predicts an increase in this transport in the absence of oxygen. Another important consideration when analysing results for the N2-O2 transition is that the model includes a number of regulatory mechanisms to describe the biological response of the yeast cell to oxygen deprivation accurately. These regulations were of two kinds. First, we modelled the induction, under anaerobic conditions, of the Rox1 responsive regulon [46,47]. Second, we modelled the effects of glucose repression on the alcohol dehydrogenase system, by adjusting the rate of degradation of the corresponding enzymes as a function of the presence or absence of oxygen [48]. This reflects the post-transcriptional regulation mechanisms involved in these processes.


A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress.

Achcar F, Camadro JM, Mestivier D - BMC Syst Biol (2011)

Variations of the fluxes in selected reactions with and without oxygen. Occurrence of 10 reactions (WT model) in the simulations, compared with experimental fluxes [36] (red). A-I: frequency of the reaction at steady state (green), J: PoP of ethanol at steady state (green).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094212&req=5

Figure 5: Variations of the fluxes in selected reactions with and without oxygen. Occurrence of 10 reactions (WT model) in the simulations, compared with experimental fluxes [36] (red). A-I: frequency of the reaction at steady state (green), J: PoP of ethanol at steady state (green).
Mentions: We then analyzed the changes in several independent elements in the simulations of physiologically relevant transitions. One of our goals was to analyse the oxidative stress response. We therefore modeled the transition from anaerobiosis to aerobiosis. We analyzed simulations in which a key element, oxygen, was removed from the model. Oxygen is required to re-oxidise the reduced equivalents produced by cell metabolism. However, S. cerevisiae can grow in the absence of oxygen, due to its ability to shift from a respiratory to a fermentative metabolism. NADH is then oxidised by alcohol dehydrogenases, leading to ethanol production. We therefore compared the simulations of our model with different levels of oxygen to the experimental data of Jouhten et al. [36], who measured metabolic fluxes in yeast cells grown in a chemostat with a limited glucose supply in the presence of 20.9% oxygen (aerobic conditions) and under anaerobic conditions (0% oxygen). These experimental conditions were taken into account by weighting "glucose import" to a low value of 0.1. We compared the experimental fluxes with the reaction frequencies at steady state of key carbon metabolism reactions, in both the presence and absence of oxygen (Figure 5). All the in silico reactions displayed patterns of variation highly similar to those observed in vivo, with the exception of the reaction catalysed by Oac1p, a mitochondrial bidirectional oxaloacetate transporter with broad specificity for various anions. There is a simple explanation for the discrepancy observed for this specific reaction (of 10 considered). In anaerobiosis, cytoplasmic oxaloacetate is less produced than in aerobiosis. However, it is also less consumed than produced. Because the ratio of the production over the consumption is higher in anaerobiosis than in aerobiosis, oxaloacetate PoP is higher. Therefore, since one of the few remaining possible reactions involving oxaloacetate is transports by OAC1, the model predicts an increase in this transport in the absence of oxygen. Another important consideration when analysing results for the N2-O2 transition is that the model includes a number of regulatory mechanisms to describe the biological response of the yeast cell to oxygen deprivation accurately. These regulations were of two kinds. First, we modelled the induction, under anaerobic conditions, of the Rox1 responsive regulon [46,47]. Second, we modelled the effects of glucose repression on the alcohol dehydrogenase system, by adjusting the rate of degradation of the corresponding enzymes as a function of the presence or absence of oxygen [48]. This reflects the post-transcriptional regulation mechanisms involved in these processes.

Bottom Line: The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments).Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data.All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Modelling in Integrative Biology, Institut Jacques Monod - UMR7592 - CNRS - Univ. Paris-Diderot, Paris, France.

ABSTRACT

Background: In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.

Results: Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating in silico mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all in silico mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.

Conclusions: All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

Show MeSH
Related in: MedlinePlus