Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2007. / Härtill 3 uppsatser.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Bioengineering, Biomedical Engineering, 2009. / Includes bibliographical references.

Cancer is a complex, heterogeneous disease and associated with a pluralism of distinct molecular events occurring on multiple layers of cell activity. It is a disease of genomic regulation driven by genetic and epigenetic mechanisms. Consideration of these regulatory levels is inevitable for understanding cancer genesis and progression. Improved high-throughput techniques developed in the last decades enable a highly resolved view on these mechanisms but at the same time the technologies produce an incredible amount of molecular data. Hence it needs advances in computational methods to master the data. In this thesis we demonstrate how to cope with high-dimensional data to characterize molecular aspects of cancer. The main aim of this thesis is to develop and to apply bioinformatics methods to unravel molecular mechanisms, with special focus on gene expression and epigenetics, underlying cancer. Therefore, we selected two cancer entities, B-cell lymphoma and glioblastoma, for a more detailed, exemplary study. Bioinformatics methods dealing with molecular cancer data have to tackle tasks like data integration, dimension reduction, data compression and proper visualization. One effective method that fulfills the mentioned tasks is self organizing map (SOM) machine learning, a technique to ‘organize’ complex, multivariate data. We present an analytic framework based on SOMs that aims at characterizing single-omics landscapes, here either regarding genome wide expression or methylation, to describe the heterogeneity of cancer on the molecular level. Molecular data of each sample is presented in terms of ‘individual’ maps, which enable their evaluation by visual inspection. The portrayal method also realizes comprehensive downstream analysis tasks such as marker selection and clustering of co-regulated features into modules, stratification of cases into subtypes, knowledge discovery, function mining and pathway analysis. Further, we describe how to detect and to correct outlier samples. In a novel combining approach all these analytic tasks of the single-omics SOM are embedded in a workflow to integratively analyze gene expression and DNA methylation data of unmatched patient cohorts. We showed that this approach provides detailed insights into the transcriptome and methylome landscapes of cancer. Furthermore, we developed a new inter-omics method based on SOM machine learning for the combined analysis of gene expression and DNA methylation data obtained from the same patient cohort. The method allows the visual inspection of the data landscapes of each sample on a personalized and class-related level, where the relative contribution of each of both data entities can be tuned either to focus on expression or methylation landscapes or on a combination of both. Using the single-omics SOM approach, we studied molecular subtypes of B-cell lymphoma based on gene expression data. The method disentangles tumor heterogeneity and provides suited markers for the cancer subtypes. We proposed a refined subtyping of B-cell lymphoma into four subtypes, rather than a previously assumed three-group classification. In a second application of the single-omics SOM we studied a gene expression data set concerning glioblastoma for which we confirmed an established four-subtype classification. Our results suggested a similar gene activation pattern as observed in the lymphoma study characterized by an antagonistic switching between transcriptional modes related to immune response and cell division. Our integrative study on a larger lymphoma cohort comprising additional subtypes confirmed previous results about the role of stemness genes during development and maturation of B-cells. Their dysfunctions in lymphoma are governed by widespread epigenetic effects altering the promoter methylation of the involved genes, their activity status as moderated by histone modifications, and also by chromatin remodeling. We identified subtype-specific signatures that associate with epigenetic effects such as remodeling from transcriptionally inactive into active chromatin states, differential promoter methylation, and the enrichment of targets of transcription factors such as EZH2 and SUZ12. While studying the transcription of epigenetic modifiers in lymphoma and healthy controls, we found that the expression levels of nearly all modifiers are strongly disturbed in lymphoma and concluded that the epigenetic machinery is highly deregulated. Our results suggested that Burkitt’s lymphoma and diffuse large B-cell lymphoma differ by an imbal-ance of repressive and poised promoters, which is associated with an imbalance of the activity of histone- and DNA-modifying enzymes. Our inter-omics method was applied to a high-grade glioblastomas. Their expression and methylation landscapes were segmented into modes of co-expressed and co-methylated genes, which reflect underlying regulatory modes of cell activity. We found antagonistic methylation and gene expression changes between the IDH1 mutated and IDH1 wild type subtypes, which affect predominantly poised and repressed chromatin states. Therefore we assume that these effects deregulate developmental processes either by their blockage or by aberrant activation. Our methods presented in this thesis enable a holistic view on high-dimensional molecular data collected in large-scale cancer studies. The examples chosen illustrate the mutual dependence of regulatory effects on genetic, epigenetic and transcriptomic levels. Our finding revealed that epigenetic deregulation in cancer must go beyond simple schemes using only a few modes of regulation. By applying the tools and methods described above to large-scale cancer cohorts we could confirm and supplement previous findings about underlying cancer biology.

Molecular mechanisms of lower-grade (II–III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context.

Mutually linked expression and methylation dynamics in the brain govern genome regulation over the whole lifetime with an impact on cognition, psychological disorders, and cancer. We performed a joint study of gene expression and DNA methylation of brain tissue originating from the human prefrontal cortex of individuals across the lifespan to describe changes in cellular programs and their regulation by epigenetic mechanisms. The analysis considers previous knowledge in terms of functional gene signatures and chromatin states derived from independent studies, aging profiles of a battery of chromatin modifying enzymes, and data of gliomas and neuropsychological disorders for a holistic view on the development and aging of the brain. Expression and methylation changes from babies to elderly adults decompose into different modes associated with the serial activation of (brain) developmental, learning, metabolic and inflammatory functions, where methylation in gene promoters mostly represses transcription. Expression of genes encoding methylome modifying enzymes is very diverse reflecting complex regulations during lifetime which also associates with the marked remodeling of chromatin between permissive and restrictive states. Data of brain cancer and psychotic disorders reveal footprints of pathophysiologies related to brain development and aging. Comparison of aging brains with gliomas supports the view that glioblastoma-like and astrocytoma-like tumors exhibit higher cellular plasticity activated in the developing healthy brain while oligodendrogliomas have a more stable differentiation hierarchy more resembling the aged brain. The balance and specific shifts between volatile and stable and between more irreversible and more plastic epigenomic networks govern the development and aging of healthy and diseased brain.

Hyaluronic acid (HA) is an important substance, which is mostly used in pharmaceutical and cosmetic industry. This substance is commonly found in the human body. HA is one of the factors contributing to virulence of microorganisms. Some bacterial strains produce hyaluronic acid in the form of a mucoid capsule that encapsulates the cell to protect bacteria against the immune system of the host organism. One of the main producers is the bacterial strain Streptococcus equi subsp. zooepidemicus. Contipro a.s. uses the strain CO4A to produce hyaluronic acid in large scale. The production strain was obtained by random mutagenesis by UV light. The aim of the work was to study changes in the genome, which led to a significant increase in hyaluronic acid production, using DNA microarray and real-time PCR (qPCR). The genome of the strain CO4A was sequenced and compared to reference ATCC35246 [1]. The size of the genome is 2,167,251 bp and 83 relevant variants (59 SNV and 34 indels) have been identified. Variants in coding regions were annotated and amino acid sequence changes were determined. In SNV mutations there was a change in the amino acid sequence in 45 cases. The change was identified in every case of indel mutations. The expression level of selected groups of genes was monitored in both strains by the method of DNA microarrays. A cascade of increased expression level of amino sugar metabolism genes leading to the synthesis of UDP-N-acetyl glucosamine was observed in strain CO4A (the increase in expression level of these genes compared to ATCC35246 was on average 28 %). Subsequently, the expression of selected genes was verified by qPCR. There was no significant difference in the expression level of the has operon genes of both strains. The effect of supplementation of the culture medium with N-acetylglucosamine (GlcNAc), which is one of the precursors of HA synthesis, was also studied by qPCR. A positive effect of the supplementation of the culture medium with external GlcNAc in the CO4A strain has been recorded. Also, the supplementation has positive effect on the yield of HA from the medium (increase in yield was on average by 17 %). GlcNAc has been shown to have a positive effect on the yield of HA in ATCC35246 strain as well (increase in yield was 9 % on average), but no significant changes in the expression levels were found in selected groups of genes in ATCC35246.