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

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.

Figure 1

Overview of the content of the model. Main pathways included in the model and their cellular localisation.

Figure 2

Graph of the model elements. Each element is a node, two elements are connected if they are involved in the same reaction. Blue nodes are constant elements, red nodes are non-constant elements. Green lines are the connections involving oxygen. The node size increases with the number of elements connected to it.

Figure 3

Histogram of the PoPs of the elements in the WT model at steady state.

Figure 4

Mean PoP of a number of elements in selected mutants. The gene was turned o after the first million of steps. A: PoP of protoporphyrin-IX when the gene HEM15 is turned o, B: superoxide anion and hydroxyl radical when the gene SOD1 is set to "OFF", C-F: PoP of glutamate, cysteine and glutathion C) when ACO1 is set to "OFF". Cysteine reached zero at steady-state, D: ACO1 is set to "OFF" and a source of glutamate is added, E: HEM1 is set to "OFF", F: HEM1 is set to "OFF" and a source of glutamate is added.

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).

Figure 6

Variations of the PoP, at steady state, of each element in the model (columns), in each in silico mutant (rows), with respect to WT simulations. For each mutant and for each element, was calculated. Positive values (red) indicate that the PoP was higher in the mutant simulations and negative values (green) imply that the PoP was higher in the WT simulations. For a complete content of all clusters see additional le 2 (cdt file of this clustering). See methods for details.

Figure 7

Variation of selected elements PoP under different hyptothesis regarding the accumulation of iron-phosphate aggregates. A: Initial Model - gene YFH1 set to "OFF", B: Hypothesis 1 - gene YFH1 set to "OFF", C: Hypothesis 1 - Element X set to "OFF". Values at steady-state: Oxydized Mir1p = 90%, non oxidized Mir1p = 20%, hydroxyl radical = 99%, FeS = 1%, D: Hypothesis 2 - gene YFH1 set to "OFF".