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Bioinformatic identification of disease associated pathways by network based analysis
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
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: urn:nbn:se:liu:diva-81898ISBN: 978-91-7519-802-6 (print)OAI: oai:DiVA.org:liu-81898DiVA: diva2:556252
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
List of papers
1. Network properties of complex human disease genes identified through genome-wide association studies
Open this publication in new window or tab >>Network properties of complex human disease genes identified through genome-wide association studies
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2009 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 11, e8090- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.

PRINCIPAL FINDINGS: We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.

CONCLUSIONS: This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

Place, publisher, year, edition, pages
San Francisco, CA San Francisco, CA, United StatesUnited States: Public Library of Science, 2009
National Category
Medical Genetics
Identifiers
urn:nbn:se:liu:diva-81894 (URN)10.1371/journal.pone.0008090 (DOI)000272828400029 ()19956617 (PubMedID)2-s2.0-77951240586 (Scopus ID)
Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
2. Network properties of human disease genes with pleiotropic effects
Open this publication in new window or tab >>Network properties of human disease genes with pleiotropic effects
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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-81895 (URN)10.1186/1752-0509-4-78 (DOI)20525321 (PubMedID)
Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
3. Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
Open this publication in new window or tab >>Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
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2012 (English)In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 13, no 6, R46- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases.

RESULTS: We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases.

CONCLUSIONS: Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.

Place, publisher, year, edition, pages
BioMed Central, 2012
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-81896 (URN)10.1186/gb-2012-13-6-r46 (DOI)000308546300006 ()22703998 (PubMedID)
Note

funding agencies|European Community|223367|US National Institutes of Health|R01-AA-0187763P20MD000516-07S1|Swedish Research Council||

Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
4. Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation
Open this publication in new window or tab >>Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation
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2012 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Although the importance of DNA methylation for mRNA expression has been shown for individualgenes in several complex diseases, such a relation has been difficult to show on a genome-wide scale.Here, we used microarrays to examine the relationship between DNA methylation and mRNAexpression in CD4+ T cells from patients with seasonal allergic rhinitis (SAR) and healthy controls.SAR is an optimal disease model because the disease process can be studied by comparing allergenchallengedCD4+ T cells obtained from patients and controls, and mimicked in Th2 polarised T cellsfrom healthy controls. The cells from patients can be analyzed to study relations between methylationand mRNA expression, while the Th2 cells can be used for functional studies. We found that DNAmethylation, but not mRNA expression clearly separated patients from controls. Similar to studies ofother complex diseases, we found no general relation between DNA methylation and mRNAexpression. However, when we took into account the absence or presence of CpG islands in thepromoters of disease associated genes an association was found: low methylation genes without CpGislands had significantly higher expression levels of disease-associated genes. This association wasconfirmed for genes whose expression levels were regulated by a transcription factor of knownrelevance for allergy, IRF4, using combined ChIP-chip and siRNA mediated silencing of IRF4expression. In summary, disease-associated increases of mRNA expression were found in lowmethylation genes without CpG islands in CD4+ T cells from patients with SAR. Further studies arewarranted to examine if a similar association is found in other complex diseases.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-81897 (URN)
Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2012-09-24Bibliographically approved

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