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Iron acquisition and oxidative stress response in aspergillus fumigatus.

Brandon M, Howard B, Lawrence C, Laubenbacher R - BMC Syst Biol (2015)

Bottom Line: In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions.Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress.This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.

View Article: PubMed Central - PubMed

Affiliation: Center for Cell Analysis and Modeling, University of Connecticut Health Center, 400 Farmington Ave, Farmington, 06030, USA. mbrandon@uchc.edu.

ABSTRACT

Background: Aspergillus fumigatus is a ubiquitous airborne fungal pathogen that presents a life-threatening health risk to individuals with weakened immune systems. A. fumigatus pathogenicity depends on its ability to acquire iron from the host and to resist host-generated oxidative stress. Gaining a deeper understanding of the molecular mechanisms governing A. fumigatus iron acquisition and oxidative stress response may ultimately help to improve the diagnosis and treatment of invasive aspergillus infections.

Results: This study follows a systems biology approach to investigate how adaptive behaviors emerge from molecular interactions underlying A. fumigatus iron regulation and oxidative stress response. We construct a Boolean network model from known interactions and simulate how changes in environmental iron and superoxide levels affect network dynamics. We propose rules for linking long term model behavior to qualitative estimates of cell growth and cell death. These rules are used to predict phenotypes of gene deletion strains. The model is validated on the basis of its ability to reproduce literature data not used in model generation.

Conclusions: The model reproduces gene expression patterns in experimental time course data when A. fumigatus is switched from a low iron to a high iron environment. In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions. Model simulations support the hypothesis that intracellular iron regulates A. fumigatus transcription factors, SreA and HapX, by a post-translational, rather than transcriptional, mechanism. Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress. This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.

No MeSH data available.


Related in: MedlinePlus

Model simulation results and experimental time course data following a switch from low iron, low superoxide to high iron, low superoxide conditions.(A) Gene expression from a qRT-PCR experiment conducted in this study. (C), (E) Gene expression from a microarray experiment by Schrettl et al., 2008 for a wild type and ΔsreA strain, respectively [13]. (B), (D), (F), (H) Simulated trajectories for corresponding model species plotted as the average state at each time step across 100 stochastic simulations. (G) All simulations were initialized from this state representing iron starvation. In (H) trajectories are generated by a model with post-translational regulation of HapX and SreA by iron (PTL) and an altered model with transcriptional regulation of hapX and sreA by iron (TS).
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Fig4: Model simulation results and experimental time course data following a switch from low iron, low superoxide to high iron, low superoxide conditions.(A) Gene expression from a qRT-PCR experiment conducted in this study. (C), (E) Gene expression from a microarray experiment by Schrettl et al., 2008 for a wild type and ΔsreA strain, respectively [13]. (B), (D), (F), (H) Simulated trajectories for corresponding model species plotted as the average state at each time step across 100 stochastic simulations. (G) All simulations were initialized from this state representing iron starvation. In (H) trajectories are generated by a model with post-translational regulation of HapX and SreA by iron (PTL) and an altered model with transcriptional regulation of hapX and sreA by iron (TS).

Mentions: We first simulated the Boolean network model of wild type A. fumigatus under each of the four possible conditions: (1) low iron and low superoxide, (2) high iron and low superoxide, (3) low iron and high superoxide, and (4) high iron and high superoxide. Figure 3 (A) - (C) show the distributions of average states across 100 wild type simulations for six selected species under three of the four conditions. Wild type distributions are not shown for the remaining condition; instead Figure 4(B) and (D) show trajectories, the average state at each time step, for eight selected species.Figure 4


Iron acquisition and oxidative stress response in aspergillus fumigatus.

Brandon M, Howard B, Lawrence C, Laubenbacher R - BMC Syst Biol (2015)

Model simulation results and experimental time course data following a switch from low iron, low superoxide to high iron, low superoxide conditions.(A) Gene expression from a qRT-PCR experiment conducted in this study. (C), (E) Gene expression from a microarray experiment by Schrettl et al., 2008 for a wild type and ΔsreA strain, respectively [13]. (B), (D), (F), (H) Simulated trajectories for corresponding model species plotted as the average state at each time step across 100 stochastic simulations. (G) All simulations were initialized from this state representing iron starvation. In (H) trajectories are generated by a model with post-translational regulation of HapX and SreA by iron (PTL) and an altered model with transcriptional regulation of hapX and sreA by iron (TS).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4418068&req=5

Fig4: Model simulation results and experimental time course data following a switch from low iron, low superoxide to high iron, low superoxide conditions.(A) Gene expression from a qRT-PCR experiment conducted in this study. (C), (E) Gene expression from a microarray experiment by Schrettl et al., 2008 for a wild type and ΔsreA strain, respectively [13]. (B), (D), (F), (H) Simulated trajectories for corresponding model species plotted as the average state at each time step across 100 stochastic simulations. (G) All simulations were initialized from this state representing iron starvation. In (H) trajectories are generated by a model with post-translational regulation of HapX and SreA by iron (PTL) and an altered model with transcriptional regulation of hapX and sreA by iron (TS).
Mentions: We first simulated the Boolean network model of wild type A. fumigatus under each of the four possible conditions: (1) low iron and low superoxide, (2) high iron and low superoxide, (3) low iron and high superoxide, and (4) high iron and high superoxide. Figure 3 (A) - (C) show the distributions of average states across 100 wild type simulations for six selected species under three of the four conditions. Wild type distributions are not shown for the remaining condition; instead Figure 4(B) and (D) show trajectories, the average state at each time step, for eight selected species.Figure 4

Bottom Line: In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions.Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress.This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.

View Article: PubMed Central - PubMed

Affiliation: Center for Cell Analysis and Modeling, University of Connecticut Health Center, 400 Farmington Ave, Farmington, 06030, USA. mbrandon@uchc.edu.

ABSTRACT

Background: Aspergillus fumigatus is a ubiquitous airborne fungal pathogen that presents a life-threatening health risk to individuals with weakened immune systems. A. fumigatus pathogenicity depends on its ability to acquire iron from the host and to resist host-generated oxidative stress. Gaining a deeper understanding of the molecular mechanisms governing A. fumigatus iron acquisition and oxidative stress response may ultimately help to improve the diagnosis and treatment of invasive aspergillus infections.

Results: This study follows a systems biology approach to investigate how adaptive behaviors emerge from molecular interactions underlying A. fumigatus iron regulation and oxidative stress response. We construct a Boolean network model from known interactions and simulate how changes in environmental iron and superoxide levels affect network dynamics. We propose rules for linking long term model behavior to qualitative estimates of cell growth and cell death. These rules are used to predict phenotypes of gene deletion strains. The model is validated on the basis of its ability to reproduce literature data not used in model generation.

Conclusions: The model reproduces gene expression patterns in experimental time course data when A. fumigatus is switched from a low iron to a high iron environment. In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions. Model simulations support the hypothesis that intracellular iron regulates A. fumigatus transcription factors, SreA and HapX, by a post-translational, rather than transcriptional, mechanism. Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress. This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.

No MeSH data available.


Related in: MedlinePlus