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Open Access Research article

Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach

Jörg Linde14*, Peter Hortschansky2, Eugen Fazius1, Axel A Brakhage24, Reinhard Guthke14 and Hubertus Haas3

Author Affiliations

1 Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Beutenbergstraße 11a, 07745 Jena, Germany

2 Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Beutenbergstraße 11a, 07745 Jena, Germany

3 Division of Molecular Biology/Biocenter, Medical University Innsbruck, Fritz-Pregl-Str.3, A-6020 Innsbruck, Austria

4 Friedrich Schiller Univiersity Jena, Jena, Germany

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BMC Systems Biology 2012, 6:6  doi:10.1186/1752-0509-6-6

Published: 19 January 2012

Abstract

Background

In System Biology, iterations of wet-lab experiments followed by modelling approaches and model-inspired experiments describe a cyclic workflow. This approach is especially useful for the inference of gene regulatory networks based on high-throughput gene expression data. Experiments can verify or falsify the predicted interactions allowing further refinement of the network model. Aspergillus fumigatus is a major human fungal pathogen. One important virulence trait is its ability to gain sufficient amounts of iron during infection process. Even though some regulatory interactions are known, we are still far from a complete understanding of the way iron homeostasis is regulated.

Results

In this study, we make use of a reverse engineering strategy to infer a regulatory network controlling iron homeostasis in A. fumigatus. The inference approach utilizes the temporal change in expression data after a change from iron depleted to iron replete conditions. The modelling strategy is based on a set of linear differential equations and offers the possibility to integrate known regulatory interactions as prior knowledge. Moreover, it makes use of important selection criteria, such as sparseness and robustness. By compiling a list of known regulatory interactions for iron homeostasis in A. fumigatus and softly integrating them during network inference, we are able to predict new interactions between transcription factors and target genes. The proposed activation of the gene expression of hapX by the transcriptional regulator SrbA constitutes a so far unknown way of regulating iron homeostasis based on the amount of metabolically available iron. This interaction has been verified by Northern blots in a recent experimental study. In order to improve the reliability of the predicted network, the results of this experimental study have been added to the set of prior knowledge. The final network includes three SrbA target genes. Based on motif searching within the regulatory regions of these genes, we identify potential DNA-binding sites for SrbA. Our wet-lab experiments demonstrate high-affinity binding capacity of SrbA to the promoters of hapX, hemA and srbA.

Conclusions

This study presents an application of the typical Systems Biology circle and is based on cooperation between wet-lab experimentalists and in silico modellers. The results underline that using prior knowledge during network inference helps to predict biologically important interactions. Together with the experimental results, we indicate a novel iron homeostasis regulating system sensing the amount of metabolically available iron and identify the binding site of iron-related SrbA target genes. It will be of high interest to study whether these regulatory interactions are also important for close relatives of A. fumigatus and other pathogenic fungi, such as Candida albicans.