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. 2015 Sep 14;10(9):e0137750.
doi: 10.1371/journal.pone.0137750. eCollection 2015.

Integrative Model of Oxidative Stress Adaptation in the Fungal Pathogen Candida albicans

Chandrasekaran Komalapriya  1 , Despoina Kaloriti  2 , Anna T Tillmann  2 , Zhikang Yin  2 , Carmen Herrero-de-Dios  2 , Mette D Jacobsen  2 , Rodrigo C Belmonte  2 , Gary Cameron  3 , Ken Haynes  4 , Celso Grebogi  5 , Alessandro P S de Moura  5 , Neil A R Gow  2 , Marco Thiel  5 , Janet Quinn  6 , Alistair J P Brown  2 , M Carmen Romano  1
Affiliations

Integrative Model of Oxidative Stress Adaptation in the Fungal Pathogen Candida albicans

Chandrasekaran Komalapriya et al. PLoS One. .

Abstract

The major fungal pathogen of humans, Candida albicans, mounts robust responses to oxidative stress that are critical for its virulence. These responses counteract the reactive oxygen species (ROS) that are generated by host immune cells in an attempt to kill the invading fungus. Knowledge of the dynamical processes that instigate C. albicans oxidative stress responses is required for a proper understanding of fungus-host interactions. Therefore, we have adopted an interdisciplinary approach to explore the dynamical responses of C. albicans to hydrogen peroxide (H2O2). Our deterministic mathematical model integrates two major oxidative stress signalling pathways (Cap1 and Hog1 pathways) with the three major antioxidant systems (catalase, glutathione and thioredoxin systems) and the pentose phosphate pathway, which provides reducing equivalents required for oxidative stress adaptation. The model encapsulates existing knowledge of these systems with new genomic, proteomic, transcriptomic, molecular and cellular datasets. Our integrative approach predicts the existence of alternative states for the key regulators Cap1 and Hog1, thereby suggesting novel regulatory behaviours during oxidative stress. The model reproduces both existing and new experimental observations under a variety of scenarios. Time- and dose-dependent predictions of the oxidative stress responses for both wild type and mutant cells have highlighted the different temporal contributions of the various antioxidant systems during oxidative stress adaptation, indicating that catalase plays a critical role immediately following stress imposition. This is the first model to encapsulate the dynamics of the transcriptional response alongside the redox kinetics of the major antioxidant systems during H2O2 stress in C. albicans.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Comprehensive reaction network model of the oxidative stress response in C. albicans.
A description of the modules and sub-modules and the list of components considered in this study are provided in Table 1. The biochemical reactions and system components are presented in S1, S2, S5 and S6 Tables. In addition to the biochemical reactions that are marked in this figure, all components of the model are also assumed to undergo a first order decay. See Model Construction for further details.
Fig 2
Fig 2. Temporal changes in the levels of stressor, glutathione, glutathione disulphide and thioredoxin following exposure to 5 mM H2O2.
Model simulations (black solid lines) are compared with the corresponding measurements based on three independent experiments (blue boxes). (a) Extracellular hydrogen peroxide (H2O2 Ex): left hand Y-axis, simulated levels (mM); right hand Y-axis, experimental measurements (percent of initial value). (b) Glutathione (GSH) left hand Y-axis, simulated levels (mM); right hand Y-axis, experimental measurements (mM). (c) Glutathione disulphide (GSSG) left hand Y-axis, simulated levels (mM); right hand Y-axis, experimental measurements (mM). (d) Simulated levels of the oxidised form of thioredoxin (Trx1Ox). Experimental errors represent standard deviations from at least three measurements.
Fig 3
Fig 3. Contributions of the three antioxidant systems to H2O2 detoxification and resistance.
(a) Experimental measurements of hydrogen peroxide levels in the medium (H2O2 Ex) of mid-exponential C. albicans cultures following exposure to 5 mM H2O2, relative to this starting H2O2 Ex concentration (%): wt, C. albicans wild type (CA372); cap1 (JC842); hog1 (JC45); cat1 (CA1864); trx1 (JC677); glr1 (glr1Δ/ glr1Δ) (S7 Table). (b) Model simulations of H2O2 Ex levels following addition of 5 mM H2O2 to C. albicans cultures. (c) Growth of serial ten-fold dilutions of C. albicans wild type, cat1, cap1, hog1, glr1 and trx1 cells on YPD plates containing H2O2 after 48 h at 30°C.
Fig 4
Fig 4. Dose-dependent response of Cap1-dependent genes following exposure to different H2O2 concentrations.
(a) Data from Enjalbert et al. [54] showing GFP expression levels from CAT1-, TTR1-, and TRX1-GFP reporters in C. albicans cells exposed to a range of H2O2 concentrations (grey scale, bottom right). GFP intensities are expressed in absorbance units. (b) Data from this study showing the levels of CAP1, CAT1 and TTR1 mRNAs in C. albicans cells exposed the same range of H2O2 concentrations. Relative mRNA levels were measured by qRT-PCR relative to the internal ACT1 mRNA control. We show data of three independent experiments and the corresponding SD (t-test). (c) Simulation results for TRR1 mRNA levels (nM) after exposure to different H2O2 concentrations, obtained using oxidative stress models that lack (2 Cap1 Forms) or include the third conceptual form of Cap1 (3 Cap1 Forms). (d) Proposed model of Cap1 regulation in C. albicans.
Fig 5
Fig 5. Temporal changes in the levels of (a) active Cap1 (Cap1A) and (b) TRR1 mRNA levels following exposure to 5 mM H2O2.
Model simulations are represented by black solid lines (left hand Y-axes), and experimental measurements by blue boxes (three independent experiments; right hand Y-axis). Relative TRR1 mRNA levels were measured relative to the internal ACT1 mRNA control. Standard deviation was calculated and is shown in the figure.
Fig 6
Fig 6. Catalase (Cat1) inactivation shifts the dose response curve to lower H2O2 concentrations.
Using qRT-PCR, relative CAP1 and TRR1 mRNA levels were measured relative to the internal ACT1 mRNA control in C. albicans cells exposed the same range of H2O2 concentrations examined in Fig 4 (grey scale, top left): upper panel, wt (wild type, CA372); lower panel, cat1 (CA1864) (S7 Table). The data is representative of three independent experiments and the standard deviation was calculated.
Fig 7
Fig 7. Perturbation of Redox Potential (ΔE) is a reasonable proxy for oxidative stress sensitivity.
(a) Simulated changes in ΔE in C. albicans cells following exposure to 5 mM H2O2: wt, wild type (CA372); cap1 (JC842); hog1 (JC45); cat1 (CA1864) and cap1 hog1 (JC118) (S7 Table). The dotted line represents -180 mV, above which cells are more likely to enter oxidant-driven cell death pathways [58,59]. (b) Experimental determination of percentage survival following exposure of the C. albicans strains to 5 mM H2O2: *, P<0.05; **, P<0.01; ***, P<0.001, using an Unpaired t-test.
Fig 8
Fig 8. The model accurately predicts the temporal protection provided by oxidative stress adaptation.
(a) Representation of the timing of the sequential stresses applied in the experiment. The white arrow represents the initial 0.4mM mM H2O2 stress (T1), whilst the colored arrows represent the addition of the second 20 mM H2O2 stress (T2) at the following times after the first stress: 0 min (black arrow); 60 min (red arrow); 120 min (grey arrow); 180 min (green arrow); 240 min (blue arrow). (b) The predicted dynamics of intracellular H2O2 levels (H2O2 In), and (c) redox potential (ΔE) after imposition of the second 20 mM H2O2 stress at: 0 min (black); 60 min (red); 180 min (green); 240 min (blue). (d) Experimentally measured survival of C. albicans cells after exposure to the sequential stresses when T2 was: 0 min (black); 60 min (red); 180 min (green); 240 min (blue). Colony forming units (CFU) for stressed cells were measured relative to the untreated control cells. The data represent mean and standard deviation values from three independent experiments: *, P<0.05; **, P<0.01.
Fig 9
Fig 9. Permeability and mRNA induction rates are the most sensitive parameters of model.
Relative error values in catalase (CAT1) at 10 min, TRR1 mRNA levels at 10 min, H2O2 Ex at 15 min, and GSH at 30 min estimated for 100 different parameter sets. Red points correspond to the case when the set of 73 key reaction rates were varied according a homogenous random distribution between 0.1 and 100 their nominal value, and blue points represents the case when both permeability and mRNA induction rates were fixed to their nominal values, whereas the rest of the parameters were randomly varied as above.
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