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This article is part of the supplement: Selected articles from The 5th IEEE International Conference on Systems Biology (ISB 2011)

Open Access Open Badges Research

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

Jing Zhao12*, Jie Chen3, Ting-Hong Yang1 and Petter Holme45

Author Affiliations

1 Department of Mathematics, Logistical Engineering University, Chongqing 400016, China

2 Department of Natural Medicinal Chemistry, Second Military Medical University, Shanghai, China

3 Department of Mathematics and Statistics, University of Missouri-Kansas City, MO 64110-2499, USA

4 Department of Physics, Umeå University, 90187 Umeå, Sweden

5 Department of Energy Science, Sungkyunkwan University, Suwon 440-746, Korea

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BMC Systems Biology 2012, 6(Suppl 1):S4  doi:10.1186/1752-0509-6-S1-S4

Published: 16 July 2012



Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown.


In this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA.


The protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction.