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  • 1.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Bioinformatic identification of disease associated pathways by network based analysis2012Doctoral 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.

    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
    Show others...
    2009 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 11, p. e8090-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: 2018-01-12Bibliographically 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
    Show others...
    2012 (English)In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 13, no 6, p. R46-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
    Show others...
    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
  • 2.
    Barrenäs, Fredrik
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Bruhn, Sören
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Jörnsten, Rebecka
    Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden.
    Langston, Michael A
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA.
    Nestor, Colm
    Östergötlands Läns Landsting.
    Rogers, Gary
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA .
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation2012Manuscript (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.

  • 3.
    Barrenäs, Fredrik
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Chavali, Sreenivas
    MRC-Laboratory of Molecular Biology, University of Cambridge, Hills Road, Cambridge, CB2 0QH, UK.
    Alves, Alexessander Couto
    Department of Genomics of Common Disease, School of Public Health, Imperial College, UK.
    Coin, Lachlan
    Department of Genomics of Common Disease, School of Public Health, Imperial College, UK.
    Jarvelin, Marjo-Riitta
    Department of Genomics of Common Disease, School of Public Health, Imperial College, UK.
    Jörnsten, Rebecka
    Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden.
    Langston, Michael A
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA .
    Ramasamy, Adaikalavan
    Department of Genomics of Common Disease, School of Public Health, Imperial College, London, UK .
    Rogers, Gary
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA .
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms2012In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 13, no 6, p. R46-Article in journal (Refereed)
    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.

  • 4.
    Barrenäs, Fredrik
    et al.
    The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
    Chavali, Sreenivas
    The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
    Holme, Petter
    Department of Physics, Umeå University, Umeå, Sweden; Department of Energy Science, Sungkyunkwan University, Suwon, Korea.
    Mobini, Reza
    The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
    Benson, Mikael
    The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
    Network properties of complex human disease genes identified through genome-wide association studies2009In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 11, p. e8090-Article in journal (Refereed)
    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.

  • 5.
    Bruhn, Sören
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Mobini, R.
    Östergötlands Läns Landsting.
    Andersson, B. A.
    Sahlgrenska University Hospital.
    Chavali, S.
    MRC Lab Molecular Biol, Cambridge, UK.
    Egan, B. S.
    Genepathway, Inc., San Diego, CA, USA.
    Hovig, E.
    Norwegian Radium Hospital.
    Sandve, G. K.
    University of Oslo.
    Langston, M. A.
    University of Tennessee, Knoxville, TN, USA.
    Rogers, G.
    University of Tennessee, Knoxville, TN, USA.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Pediatrics.
    Increased expression of IRF4 and ETS1 in CD4+ cells from patients with intermittent allergic rhinitis2012In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 67, no 1, p. 33-40Article in journal (Refereed)
    Abstract [en]

    Background: The transcription factor (TF) IRF4 is involved in the regulation of Th1, Th2, Th9, and Th17 cells, and animal studies have indicated an important role in allergy. However, IRF4 and its target genes have not been examined in human allergy. Methods: IRF4 and its target genes were examined in allergen-challenged CD4+ cells from patients with IAR, using combined gene expression microarrays and chromatin immunoprecipitation chips (ChIP-chips), computational target prediction, and RNAi knockdowns. Results: IRF4 increased in allergen-challenged CD4+ cells from patients with IAR, and functional studies supported its role in Th2 cell activation. IRF4 ChIP-chip showed that IRF4 regulated a large number of genes relevant to Th cell differentiation. However, neither Th1 nor Th2 cytokines were the direct targets of IRF4. To examine whether IRF4 induced Th2 cytokines via one or more downstream TFs, we combined gene expression microarrays, ChIP-chips, and computational target prediction and found a putative intermediary TF, namely ETS1 in allergen-challenged CD4+ cells from allergic patients. ETS1 increased significantly in allergen-challenged CD4+ cells from patients compared to controls. Gene expression microarrays before and after ETS1 RNAi knockdown showed that ETS1 induced Th2 cytokines as well as disease-related pathways. Conclusions: Increased expression of IRF4 in allergen-challenged CD4+ cells from patients with intermittent allergic rhinitis leads to activation of a complex transcriptional program, including Th2 cytokines.

  • 6.
    Bruhn, Sören
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Fang, Yu
    Guiyang Medical Coll, Peoples R China University of Gothenburg, Sweden .
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Konstantinell, Aelita
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Kronke, Andrea
    Cenix BioScience GmbH, Germany .
    Sonnichsen, Birte
    Cenix BioScience GmbH, Germany .
    Bresnick, Anne
    Albert Einstein Coll Med, NY 10461 USA .
    Dulyaninova, Natalya
    Albert Einstein Coll Med, NY 10461 USA .
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhao, Yelin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Klingelhofer, Jorg
    University of Copenhagen, Denmark .
    Ambartsumian, Noona
    University of Copenhagen, Denmark .
    Beck, Mette K.
    Technical University of Denmark, Denmark .
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Bona, Elsa
    Boras Hospital, Sweden .
    Xiang, Zou
    University of Gothenburg, Sweden .
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    A Generally Applicable Translational Strategy Identifies S100A4 as a Candidate Gene in Allergy2014In: Science Translational Medicine, ISSN 1946-6234, E-ISSN 1946-6242, Vol. 6, no 218Article in journal (Refereed)
    Abstract [en]

    The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (T(H)2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy.

  • 7.
    Chavali, Sreenivas
    et al.
    The Unit for Clinical Systems Biology, University of Gothenburg, Sweden.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Kanduri, Kartiek
    The Unit for Clinical Systems Biology, University of Gothenburg, Sweden.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Network properties of human disease genes with pleiotropic effects2010In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 4, no 78Article in journal (Refereed)
    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.

  • 8.
    Chavali, Sreenivas
    et al.
    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.
    Bruhn, Sören
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Tiemann, Katrin
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Sætrom, Pål
    Norwegian University of Science and Technology, Trondheim, Norway.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Saito, Takaya
    Norwegian University of Science and Technology, Trondheim, Norway.
    Kanduri, Kartiek
    University of Gothenburg, Sweden .
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases2013In: RNA: A publication of the RNA Society, ISSN 1355-8382, E-ISSN 1469-9001, Vol. 19, no 11, p. 1552-1562Article in journal (Refereed)
    Abstract [en]

    MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions.

  • 9.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Edström, Måns
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences.
    Gawel, Danuta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Nestor, Colm
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Tojo, James
    Karolinska Institute, Sweden Centre Molecular Med, Sweden .
    Kockum, Ingrid
    Karolinska Institute, Sweden Centre Molecular Med, Sweden .
    Olsson, Tomas
    Karolinska Institute, Sweden Centre Molecular Med, Sweden .
    Serra-Musach, Jordi
    IDIBELL, Spain .
    Bonifaci, Nuria
    IDIBELL, Spain .
    Angel Pujana, Miguel
    IDIBELL, Spain .
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Inflammation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment2014In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 6, no 17Article in journal (Refereed)
    Abstract [en]

    Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

  • 10.
    Nestor, Colm
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Lentini, Antonio
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Zhang, Huan
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Bruhn, Sören
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Jornsten, Rebecka
    University of Gothenburg, Sweden .
    Langston, Michael A.
    University of Tennessee, TN USA .
    Rogers, Gary
    University of Tennessee, TN USA .
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    DNA Methylation Changes Separate Allergic Patients from Healthy Controls and May Reflect Altered CD4(+) T-Cell Population Structure2014In: PLoS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 10, no 1, p. e1004059-Article in journal (Refereed)
    Abstract [en]

    Altered DNA methylation patterns in CD4(+) T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its welldefined phenotype and etiology. We generated genome-wide DNA methylation (N-patients = 8, N-controls = 8) and gene expression (N-patients = 9, N-controls = 10) profiles of CD4(+) T-cells from SAR patients and healthy controls using Illuminas HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (N-patients = 12, N-controls = 12), but not by gene expression (N-patients = 21, N-controls = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (N-patients = 35) and controls (N-controls = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4(+) T cells.

  • 11.
    Schoenrock, Andrew
    et al.
    Carleton University, Ottawa, Canada.
    Samanfar, Bahram
    Carleton University, Ottawa, Canada.
    Pitre, Sylvain
    Carleton University, Ottawa, Canada.
    Hooshyar, Mohsen
    Carleton University, Ottawa, Canada.
    Jin, Ke
    University of Toronto, Toronto, Canada.
    Phillips, Charles A
    University of Tennessee, Knoxville, Tennessee, USA.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Gothenburg University, Gothenburg, Sweden.
    Phanse, Sadhna
    University of Toronto, Toronto, Canada.
    Omidi, Katayoun
    University of Toronto, Toronto, Canada.
    Gui, Yuan
    University of Toronto, Toronto, Canada.
    Alamgir, Md
    University of Toronto, Toronto, Canada.
    Wong, Alex
    University of Toronto, Toronto, Canada.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Gothenburg University, Gothenburg, Sweden.
    Babu, Mohan
    University of Regina, Regina, Saskatchewan, Canada.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping. Gothenburg University, Gothenburg, Sweden.
    Langston, Michael A
    Carleton University, Ottawa, Canada.
    Green, James R
    Carleton University, Ottawa, Canada.
    Dehne, Frank
    Carleton University, Ottawa, Canada.
    Golshani, Ashkan
    Carleton University, Ottawa, Canada.
    Efficient prediction of human protein-protein interactions at a global scale2014In: BMC bioinformatics, ISSN 1471-2105, Vol. 15, no 1, p. 383-Article in journal (Refereed)
    Abstract [en]

    BackgroundOur knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.ResultsOn the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.ConclusionsThe speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

  • 12.
    Sjogren, A-K M
    et al.
    University of Gothenburg.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Muraro, A
    Padua Gen University Hospital.
    Gustafsson, Mika
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Saetrom, P
    University of Gothenburg.
    Wang, Hui
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Monozygotic twins discordant for intermittent allergic rhinitis differ in mRNA and protein levels2012In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 67, no 6, p. 831-833Article in journal (Refereed)
    Abstract [en]

    Monozygotic (MZ) twins discordant for complex diseases may help to find disease mechanisms that are not due to genetic variants. Intermittent allergic rhinitis (IAR) is an optimal disease model because it occurs at defined time points each year, owing to known external antigens. We hypothesized that MZ twins discordant for IAR could help to find gene expression differences that are not dependent on genetic variants. We collected blood outside of the season from MZ twins discordant for IAR, challenged their peripheral blood mononuclear cells (PBMC) with pollen allergen in vitro, collected supernatants and isolated CD4+ T cells. We identified disease-relevant mRNAs and proteins that differed between the discordant MZ twins. By contrast, no differences in microRNA expression were found. Our results indicate that MZ twins discordant for IAR is an optimal model to identify disease mechanisms that are not due to genetic variants.

  • 13.
    Wang, Hui
    et al.
    Unit for Clinical Systems Biology, Queen Silvia Children's Hospital, University of Gothenburg, Gothenburg, Sweden.
    Barrenäs, Fredrik
    Unit for Clinical Systems Biology, Queen Silvia Children's Hospital, University of Gothenburg, Gothenburg, Sweden.
    Bruhn, Sören
    Unit for Clinical Systems Biology, Queen Silvia Children's Hospital, University of Gothenburg, Gothenburg, Sweden.
    Mobini, Reza
    Unit for Clinical Systems Biology, Queen Silvia Children's Hospital, University of Gothenburg, Gothenburg, Sweden.
    Benson, Mikael
    Unit for Clinical Systems Biology, Queen Silvia Children's Hospital, University of Gothenburg, Gothenburg, Sweden.
    Increased IFN-gamma activity in seasonal allergic rhinitis is decreased by corticosteroid treatment2009In: Journal of Allergy and Clinical Immunology, ISSN 0091-6749, E-ISSN 1097-6825, Vol. 124, no 6, p. 1360-1362Article in journal (Refereed)
  • 14.
    Wang, Hui
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Pediatrics. Linköping University, Faculty of Health Sciences.
    Gottfries, Johan
    University of Gothenburg.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Pediatrics. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Pediatrics. Linköping University, Faculty of Health Sciences.
    Identification of Novel Biomarkers in Seasonal Allergic Rhinitis by Combining Proteomic, Multivariate and Pathway Analysis2011In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 6, no 8, p. e23563-Article in journal (Refereed)
    Abstract [en]

    Background: Glucocorticoids (GCs) play a key role in the treatment of seasonal allergic rhinitis (SAR). However, some patients show a low response to GC treatment. We hypothesized that proteins that correlated to discrimination between symptomatic high and low responders (HR and LR) to GC treatment might be regulated by GCs and therefore suitable as biomarkers for GC treatment.

    Methodology/Principal Findings: We identified 953 nasal fluid proteins in symptomatic HR and LR with a LC MS/MS based-quantitative proteomics analysis and performed multivariate analysis to identify a combination of proteins that best separated symptomatic HR and LR. Pathway analysis showed that those proteins were most enriched in the acute phase response pathway. We prioritized candidate biomarkers for GC treatment based on the multivariate and pathway analysis. Next, we tested if those candidate biomarkers differed before and after GC treatment in nasal fluids from 40 patients with SAR using ELISA. Several proteins including ORM (P<0.0001), APOH (P<0.0001), FGA (P<0.01), CTSD (P<0.05) and SERPINB3 (P<0.05) differed significantly before and after GC treatment. Particularly, ORM (P<0.01), FGA (P<0.05) and APOH (P<0.01) that belonged to the acute phase response pathway decreased significantly in HR but not LR before and after GC treatment.

    Conclusions/Significance: We identified several novel biomarkers for GC treatment response in SAR with combined proteomics, multivariate and pathway analysis. The analytical principles may be generally applicable to identify biomarkers in clinical studies of complex diseases.

  • 15.
    Wang, Kai
    et al.
    University of Tennessee, Knoxville, USA.
    Phillips, Charles A
    University of Tennessee, Knoxville, USA.
    Rogers, Gary L
    National Institute for Computational Sciences, Oak Ridge, Tennessee, USA.
    Barrenäs, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Benson, Mikael
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Allergy Center. Östergötlands Läns Landsting, Center of Paediatrics and Gynaecology and Obstetrics, Department of Paediatrics in Linköping.
    Langston, Michael A
    University of Tennessee, Knoxville, USA.
    Differential Shannon entropy and differential coefficient of variation: alternatives and augmentations to differential expression in the search for disease-related genes2014In: International journal of computational biology and drug design, ISSN 1756-0756, Vol. 7, no 2-3, p. 183-94Article in journal (Refereed)
    Abstract [en]

    Differential expression has been a standard tool for analysing case-control transcriptomic data since the advent of microarray technology. It has proved invaluable in characterising the molecular mechanisms of disease. Nevertheless, the expression profile of a gene across samples can be perturbed in ways that leave the expression level unaltered, while a biological effect is nonetheless present. This paper describes and analyses differential Shannon entropy and differential coefficient of variation, two alternate techniques for identifying genes of interest. Ontological analysis across 16 human disease datasets demonstrates that these alternatives are effective at identifying disease-related genes not found by mere differential expression alone. Because the two alternate techniques are based on somewhat different mathematical formulations, they tend to produce somewhat different gene lists. Moreover, each may pinpoint genes completely overlooked by the other. Thus, measures of entropy and variation can be used to replace or better yet augment standard differential expression computations.

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