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- Barrenäs, Fredrik, 1981-
(författare)
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Bioinformatic identification of disease associated pathways by network based analysis
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Barrenäs, Fredrik, et al.
(författare)
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Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation
- 2012
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
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| 4. |
- Barrenäs, Fredrik, et al.
(författare)
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Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
- 2012
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Ingår i: Genome Biology. - : BioMed Central. - 1465-6906 .- 1474-760X .- 1465-6914. ; 13:6, s. R46-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
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| 6. |
- Benson, Mikael, et al.
(författare)
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Systembiologin kan förändra sjukvården radikalt. Ger underlag för individualiserad prediktion, prevention och behandling : [Systems biology can radically change health care. Basis for individualized prediction, prevention and treatment]
- 2007
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Ingår i: Läkartidningen. - : Läkartidningen. - 0023-7205 .- 1652-7518. ; 104:42, s. 3037-3041
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Tidskriftsartikel (refereegranskat)abstract
- Vanliga sjukdomar som allergi, diabetes och cancer är komplexa, dvs de beror på obalans mellan ett stort antal gener och miljöfaktorer, snarare än på enskilda »felande« gener. Det finns teknik för att samtidigt analysera mRNA- och proteinuttryck för alla människans gener. Detta har resulterat i väldiga datamängder, som kan bidra till att öka förståelsen av komplexa sjukdomar. Problemet är att strukturera och tolka informationen.Systembiologi syftar till att konstruera ett teoretiskt ramverk för att beskriva hur molekylära signalvägar och nätverk, snarare än enskilda molekyler, orsakar sjukdom. Detta har börjat ge kliniska resultat, tex individualiserad medicinering vid behandling av cancer.Inom de närmsta decennierna förutspås systembiologisk forskning få klinisk betydelse, inte bara för att individualisera behandling, utan även för att förutsäga eller förebygga sjukdomar. Läkemedelsindustrin gör också stora systembiologiska satsningar för att utveckla nya mediciner.Det är därför angeläget att svenska kliniker, forskare och beslutsfattare snarast tar ställning till hur klinisk systembiologisk forskning ska bedrivas i Sverige.I denna artikel ges en introduktion till nya tekniker för att samtidigt studera människans alla gener. Dessutom beskrivs systembiologiska principer för att tolka resultaten och vilka kliniska konsekvenser detta kan få.
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| 7. |
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| 8. |
- Clermont, Gilles, et al.
(författare)
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Bridging the gap between systems biology and medicine
- 2009
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Ingår i: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 1:9
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Tidskriftsartikel (refereegranskat)abstract
- ABSTRACT : Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.
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| 9. |
- Jay, Jeremy J., et al.
(författare)
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A systematic comparison of genome-scale clustering algorithms
- 2012
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Background: A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods: For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each clusters agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results: Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions: Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further development and application of combinatorial strategies is warranted.
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| 10. |
- Kim, Haesook T., et al.
(författare)
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Prognostic Score and Cytogenetic Risk Classification for Chronic Lymphocytic Leukemia Patients : Center for International Blood and Marrow Transplant Research Report
- 2019
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Ingår i: Clinical Cancer Research. - : AMER ASSOC CANCER RESEARCH. - 1078-0432 .- 1557-3265. ; 25:16, s. 5143-5155
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop a prognostic model and cytogenetic risk classification for previously treated patients with chronic lymphocytic leukemia (CLL) undergoing reduced intensity conditioning (RIC) allogeneic hematopoietic cell transplantation (HCT).Experimental Design: We performed a retrospective analysis of outcomes of 606 patients with CLL who underwent RIC allogeneic HCT between 2008 and 2014 reported to the Center for International Blood and Marrow Transplant Research.Results: On the basis of multivariable models, disease status, comorbidity index, lymphocyte count, and white blood cell count at HCT were selected for the development of prognostic model. Using the prognostic score, we stratified patients into low-, intermediate-, high-, and very-high-risk [4-year progression-free survival (PFS) 58%, 42%, 33%, and 25%, respectively, P < 0.0001; 4-year overall survival (OS) 70%, 57%, 54%, and 38%, respectively, P < 0.0001]. We also evaluated karyotypic abnormalities together with del(17p) and found that del(17p) or >= 5 abnormalities showed inferior PFS. Using a multivariable model, we classified cytogenetic risk into low, intermediate, and high (P < 0.0001). When the prognostic score and cytogenetic risk were combined, patients with low prognostic score and low cytogenetic risk had prolonged PFS (61% at 4 years) and OS (75% at 4 years).Conclusions: In this large cohort of patients with previously treated CLL who underwent RIC HCT, we developed a robust prognostic scoring system of HCT outcomes and a novel cytogenetic-based risk stratification system. These prognostic models can be used for counseling patients, comparing data across studies, and providing a benchmark for future interventions. For future study, we will further validate these models for patients receiving targeted therapies prior to HCT.
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