Linde Jörg, Hortschansky Peter, Fazius Eugen, Brakhage Axel A, Guthke Reinhard, Haas Hubertus
Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute, Jena, Germany.
BMC Syst Biol. 2012 Jan 19;6:6. doi: 10.1186/1752-0509-6-6.
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.
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.
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.
在系统生物学中，湿实验室实验、建模方法以及受模型启发的实验的迭代描述了一个循环工作流程。这种方法对于基于高通量基因表达数据推断基因调控网络特别有用。实验可以验证或证伪预测的相互作用，从而进一步完善网络模型。烟曲霉是一种主要的人类真菌病原体。一个重要的毒力特性是其在感染过程中获取足够量铁的能力。尽管已知一些调控相互作用，但我们对铁稳态的调控方式仍远未完全理解。
在本研究中，我们利用逆向工程策略推断控制烟曲霉铁稳态的调控网络。该推断方法利用从缺铁条件转变为富铁条件后表达数据的时间变化。建模策略基于一组线性微分方程，并提供了将已知调控相互作用作为先验知识进行整合的可能性。此外，它利用了重要的选择标准，如稀疏性和稳健性。通过编制烟曲霉铁稳态已知调控相互作用的列表，并在网络推断过程中对其进行软整合，我们能够预测转录因子与靶基因之间的新相互作用。转录调节因子SrbA对hapX基因表达的拟激活作用构成了一种迄今未知的基于代谢可利用铁量调节铁稳态的方式。最近的一项实验研究通过Northern印迹法验证了这种相互作用。为了提高预测网络的可靠性，该实验研究的结果已被添加到先验知识集合中。最终网络包括三个SrbA靶基因。基于在这些基因调控区域内的基序搜索，我们确定了SrbA潜在的DNA结合位点。我们的湿实验室实验证明了SrbA对hapX、hemA和srbA启动子具有高亲和力结合能力。
本研究展示了典型系统生物学循环的一个应用，并且基于湿实验室实验人员和计算机模拟建模人员之间的合作。结果强调在网络推断过程中使用先验知识有助于预测生物学上重要的相互作用。结合实验结果，我们指出了一种新的铁稳态调节系统，该系统可感知代谢可利用铁的量，并确定了与铁相关的SrbA靶基因的结合位点。研究这些调控相互作用对于烟曲霉的近亲以及其他致病真菌（如白色念珠菌）是否也很重要将具有很高的研究价值。
