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Network properties of human disease genes with pleiotropic effects
The Unit for Clinical Systems Biology, University of Gothenburg, Sweden.
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences. (The Unit for Clinical Systems Biology, University of Gothenburg, Sweden)
The Unit for Clinical Systems Biology, University of Gothenburg, Sweden.
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
2010 (English)In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 4, no 78Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: The ability of a gene to cause a disease is known to be associated with the topological position of its protein product in the molecular interaction network. Pleiotropy, in human genetic diseases, refers to the ability of different mutations within the same gene to cause different pathological effects. Here, we hypothesized that the ability of human disease genes to cause pleiotropic effects would be associated with their network properties.

RESULTS: Shared genes, with pleiotropic effects, were more central than specific genes that were associated with one disease, in the protein interaction network. Furthermore, shared genes associated with phenotypically divergent diseases (phenodiv genes) were more central than those associated with phenotypically similar diseases. Shared genes had a higher number of disease gene interactors compared to specific genes, implying higher likelihood of finding a novel disease gene in their network neighborhood. Shared genes had a relatively restricted tissue co-expression with interactors, contrary to specific genes. This could be a function of shared genes leading to pleiotropy. Essential and phenodiv genes had comparable connectivities and hence we investigated for differences in network attributes conferring lethality and pleiotropy, respectively. Essential and phenodiv genes were found to be intra-modular and inter-modular hubs with the former being highly co-expressed with their interactors contrary to the latter. Essential genes were predominantly nuclear proteins with transcriptional regulation activities while phenodiv genes were cytoplasmic proteins involved in signal transduction.

CONCLUSION: The properties of a disease gene in molecular interaction network determine its role in manifesting different and divergent diseases.

Place, publisher, year, edition, pages
2010. Vol. 4, no 78
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-81895DOI: 10.1186/1752-0509-4-78PubMedID: 20525321OAI: oai:DiVA.org:liu-81895DiVA: diva2:556242
Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Bioinformatic identification of disease associated pathways by network based analysis
Open this publication in new window or tab >>Bioinformatic identification of disease associated pathways by network based analysis
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many common diseases are complex, meaning that they are caused by many interacting genes. This makes them difficult to study; to determine disease mechanisms, disease-associated genes must be analyzed in combination. Disease-associated genes can be detected using high-throughput methods, such as mRNA expression microarrays, DNA methylation microarrays and genome-wide association studies (GWAS), but determining how they interact to cause disease is an intricate challenge. One approach is to organize disease-associated genes into networks using protein-protein interactions (PPIs) and dissect them to identify disease causing pathways. Studies of complex disease can also be greatly facilitated by using an appropriate model system. In this dissertation, seasonal allergic rhinitis (SAR) served as a model disease. SAR is a common disease that is relatively easy to study. Also, the key disease cell types, like the CD4+ T cell, are known and can be cultured and activated in vitro by the disease causing pollen.

The aim of this dissertation was to determine network properties of disease-associated genes, and develop methods to identify and validate networks of disease-associated genes. First, we showed that disease-associated genes have distinguishing network properties, one being that they co-localize in the human PPI network. This supported the existence of disease modules within the PPI network. We then identified network modules of genes whose mRNA expression was perturbed in human disease, and showed that the most central genes in those network modules were enriched for disease-associated polymorphisms identified by GWAS. As a case study, we identified disease modules using mRNA expression data from allergen-challenged CD4+ cells from patients with SAR. The case study identified and validated a novel disease-associated gene, FGF2 using GWAS data and RNAi mediated knockdown.

Lastly, we examined how DNA methylation caused disease-associated mRNA expression changes in SAR. DNA methylation, but not mRNA expression profiles, could accurately distinguish allergic patients from healthy controls. Also, we found that disease-associated mRNA expression changes were associated with a low DNA methylation content and absence of CpG islands. Specifically within this group, we found a correlation between disease-associated mRNA expression changes and DNA methylation changes. Using ChIP-chip analysis, we found that targets of a known disease relevant transcription factor, IRF4, were also enriched among non CpG island genes with low methylation levels.

Taken together, in this dissertation the network properties of disease-associated genes were examined, and then used to validate disease networks defined by mRNA expression data. We then examined regulatory mechanisms underlying disease-associated mRNA expression changes in a model disease. These studies support network-based analyses as a method to understand disease mechanisms and identify important disease causing genes, such as treatment targets or markers for personalized medication.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 48 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1326
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-81898 (URN)978-91-7519-802-6 (ISBN)
Public defence
2012-10-12, Linden, Hälsouniversitetet, Campus US, Linköpings universitet, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2012-09-24Bibliographically approved

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