Search: id:"swepub:oai:DiVA.org:liu-81898" >
Bioinformatic ident...
Bioinformatic identification of disease associated pathways by network based analysis
-
- Barrenäs, Fredrik, 1981- (author)
- Linköpings universitet,Institutionen för klinisk och experimentell medicin,Hälsouniversitetet
-
- Benson, Mikael, Professor (thesis advisor)
- Linköpings universitet,Institutionen för klinisk och experimentell medicin,Hälsouniversitetet
-
- Langston, Michael (thesis advisor)
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA
-
show more...
-
- Komorowski, Jan, Professor (opponent)
- The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden
-
show less...
-
(creator_code:org_t)
- ISBN 9789175198026
- Linköping : Linköping University Electronic Press, 2012
- English 48 s.
-
Series: Linköping University Medical Dissertations, 0345-0082 ; 1326
- Related links:
-
https://liu.diva-por... (primary) (Raw object)
-
show more...
-
https://liu.diva-por...
-
https://urn.kb.se/re...
-
show less...
Abstract
Subject headings
Close
- 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.
Publication and Content Type
- vet (subject category)
- dok (subject category)
Find in a library
To the university's database