The effects of maternal hyperglycemia on human umbilical vascular gene expression and neonatal rat lung development

Background: Maternal diabetes affects many fetal organ systems, including the vasculature and the lungs. The offspring of diabetic mothers have respiratory adaptation problems after birth. The mechanisms are multifactorial and the effects are prolonged during the postnatal period. An increasing incidence of diabetic pregnancies accentuates the importance of identifying the pathological mechanisms, which cause the metabolic and genetic changes that occur in offspring, born to diabetic mothers. Aims and methods: The aim of this thesis was to determine changes both in human umbilical cord exposed to maternal type 1 diabetes and in neonatal rat lungs after streptozotocin-induced maternal hyperglycemia, during pregnancy. Rat lungs were used as a model for the potential disease mechanisms. Gene expression alterations were determined in human umbilical cords at birth and in rat pup lungs at two week of age. During the first two postnatal weeks, rat lung development was studied morphologically and histologically. Further, the effect of postnatal hyperoxia on hyperglycemia-primed rat lungs was investigated at one week of age to mimic the clinical situation of supplemental oxygen treatment. Results: In the umbilical cord, maternal diabetes had a major negative effect on the expression of genes involved in blood vessel development. The genes regulating vascular tone were also affected. In neonatal rat lungs, intrauterine hyperglycemia had a prolonged effect on gene expression during late alveolarization. The most affected pathway was the upregulation of extracellular matrix proteins. Newborn rat lungs exposed to intrauterine hyperglycemia had thinner saccular walls without changes in airspace size, a smaller relative lung weight and lung total tissue area, and increased cellular apoptosis and proliferation compared to control lungs, possibly reflecting an aberrant maturational adaptation. At one and two weeks of age, cell proliferation and secondary crest formation were accelerated in hyperglycemia-exposed lungs. Postnatal hyperoxic exposure, alone caused arrested alveolarization with thin-walled and enlarged alveoli. In contrast, the dual exposure of intrauterine hyperglycemia and postnatal hyperoxia resulted in the phenotype of thick septa together with arrested alveolarization and decreased number of small pulmonary arteries. Conclusions: Maternal diabetic environment seems to alter the umbilical cord gene expression profile of the regulation of vascular development and function. Fetal hyperglycemia may additionally affect the genetic regulation of the postnatal lung development and may actually induce prolonged structural alterations in neonatal lungs together with a modifying effect on the deleterious pulmonary exposure of postnatal hyperoxia. This, combined with the novel human umbilical cord gene data could serve as stepping stones for future therapies to curb developmental aberrations.

Regulation of B Cell Gene Expression and Function by Ikaros, Helios and Bcl6

B lymphocytes constitute a key branch of adaptive immunity by providing specificity to recognize a vast variety of antigens by B cell antigen receptors (BCR) and secreted antibodies. Antigen recognition activates the cells and can produce antibody secreting plasma cells via germinal center reaction that leads to the maturation of antigen recognition affinity and switching of antibody effector class. The specificity of antigen recognition is achieved through a multistep developmental pathway that is organized by interplay of transcription factors and signals through BCR. Lymphoid malignancies arise from different stages of development in abnormal function of transcriptional regulation. To understand the B cell development and the function of B cells, a thorough understanding of the regulation of gene expression is important. The transcription factors of the Ikaros family and Bcl6 are frequently associated with lymphoma generation. The aim of this study was to reveal the targets of Ikaros, Helios and Bcl6 mediated gene regulation and to find out the function of Ikaros and Helios in B cells. This study uses gene targeted DT40 B cell lines and establishes a role for Ikaros family factors Ikaros and Helios in the regulation of BCR signaling that is important at developmental checkpoints, for cell survival and in activation. Ikaros and Helios had opposing roles in the regulation of BCR signals. Ikaros was found to directly repress the SHIP gene that encodes a signaling lipid-metabolizing enzyme, whereas Helios had activating effect on SHIP expression. The findings demonstrate a balancing function for these two Ikaros family transcription factors in the regulation of BCR signaling as well as in the regulation of gene expression. Bcl6 was found to repress plasma cell gene expression program while maintaining gene expression profile of B cells. Analysis of direct Bcl6 target genes suggested novel mechanisms for Bcl6-mediated suppression of plasma cell differentiation and promoting germinal center phenotype.

Fibroblast Growth Factor 8 and its Receptors in The Growth and Angiogenesis of Experimental Breast Cancer . With Special Reference to Thrombospondin-1 as a Novel Target Gene for FGF-8

The growth of breast cancer is regulated by hormones and growth factors. Recently, aberrant fibroblast growth factor (FGF) signalling has been strongly implicated in promoting the progression of breast cancer and is thought to have a role in the development of endocrine resistant disease. FGFs mediate their auto- and paracrine signals through binding to FGF receptors 1-4 (FGFR1-4) and their isoforms. Specific targets of FGFs in breast cancer cells and the differential role of FGFRs, however, are poorly described. FGF-8 is expressed at elevated levels in breast cancer, and it has been shown to act as an angiogenic, growth promoting factor in experimental models of breast cancer. Furthermore, it plays an important role in mediating androgen effects in prostate cancer and in some breast cancer cell lines. We aimed to study testosterone (Te) and FGF-8 regulated genes in Shionogi 115 (S115) breast cancer cells, characterise FGF-8 activated intracellular signalling pathways and clarify the role of FGFR1, -2 and -3 in these cells. Thrombospondin-1 (TSP-1), an endogenous inhibitor of angiogenesis, was recognised as a Te and FGF-8 regulated gene. Te repression of TSP-1 was androgen receptor (AR)-dependent. It required de novo protein synthesis, but it was independent of FGF-8 expression. FGF-8, in turn, downregulated TSP-1 transcription by activating the ERK and PI3K pathways, and the effect could be reversed by specific kinase inhibitors. Differential FGFR1-3 action was studied by silencing each receptor by shRNA expression in S115 cells. FGFR1 expression was a prerequisite for the growth of S115 tumours, whereas FGFR2 expression alone was not able to promote tumour growth. High FGFR1 expression led to a growth advantage that was associated with strong ERK activation, increased angiogenesis and reduced apoptosis, and all of these effects could be reversed by an FGFR inhibitor. Taken together, the results of this thesis show that FGF-8 and FGFRs contribute strongly to the regulation of the growth and angiogenesis of experimental breast cancer and support the evidence for FGF-FGFR signalling as one of the major players in breast cancers.

Herpes simplex virus type 1 (HSV-1) pathogenesis and HSV gene therapy of experimental autoimmune encephalomyelitis

The aim of this thesis was to develop new herpes simplex virus (HSV) vectors for gene therapy of experimental autoimmune encephalomyelitis (EAE), the principal model of multiple sclerosis (MS), and to study the pathogenesis of wild-type HSV-1 and HSV-1 vectors in vivo. By introducing potential immunomodulatory factors into mice with EAE we strived to develop therapies and possibly find molecules improving recovery from EAE. We aimed at altering the immune response by inducing favorable Th2-type cytokines, thus shifting the immune response from a Th1- or a Th17-response. Our HSV vector expressing interleukin (IL)-5 modulated the cytokine responses, decreased inflammation and alleviated EAE. The use of a novel method, bacterial artificial chromosome (BAC), for engineering recombinant HSV facilitated the construction of a new vector expressing leukemia inhibitory factor (LIF). LIF is a neurotropic cytokine with broad functions in the central nervous system (CNS). LIF promotes oligodendrocyte maturation and decreases demyelination and oligodendrocyte loss. The BAC-derived HSV-LIF vector alleviated the clinical symptoms, induced a higher number of oligodendrocytes and modulated T cell responses. By administering HSV via different infection routes, e.g. peripherally via the nose or eye, or intracranially to the brain, the effect of the immune response on HSV spread at different points of the natural infection route was studied. The intranasal infection was an effective delivery route of HSV to the trigeminal ganglion and CNS, whereas corneal infection displayed limited spread. The corneal and intranasal infections induced different peripheral immune responses, which might explain the observed differences in viral spread.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Computational models for and from biology : simple gene assembly and reaction systems

The advancement of science and technology makes it clear that no single perspective is any longer sufficient to describe the true nature of any phenomenon. That is why the interdisciplinary research is gaining more attention overtime. An excellent example of this type of research is natural computing which stands on the borderline between biology and computer science. The contribution of research done in natural computing is twofold: on one hand, it sheds light into how nature works and how it processes information and, on the other hand, it provides some guidelines on how to design bio-inspired technologies. The first direction in this thesis focuses on a nature-inspired process called gene assembly in ciliates. The second one studies reaction systems, as a modeling framework with its rationale built upon the biochemical interactions happening within a cell. The process of gene assembly in ciliates has attracted a lot of attention as a research topic in the past 15 years. Two main modelling frameworks have been initially proposed in the end of 1990s to capture ciliates’ gene assembly process, namely the intermolecular model and the intramolecular model. They were followed by other model proposals such as templatebased assembly and DNA rearrangement pathways recombination models. In this thesis we are interested in a variation of the intramolecular model called simple gene assembly model, which focuses on the simplest possible folds in the assembly process. We propose a new framework called directed overlap-inclusion (DOI) graphs to overcome the limitations that previously introduced models faced in capturing all the combinatorial details of the simple gene assembly process. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs. We also introduce DOI graph-based rewriting rules that capture all the operations of the simple gene assembly model and prove that they are equivalent to the string-based formalization of the model. Reaction systems (RS) is another nature-inspired modeling framework that is studied in this thesis. Reaction systems’ rationale is based upon two main regulation mechanisms, facilitation and inhibition, which control the interactions between biochemical reactions. Reaction systems is a complementary modeling framework to traditional quantitative frameworks, focusing on explicit cause-effect relationships between reactions. The explicit formulation of facilitation and inhibition mechanisms behind reactions, as well as the focus on interactions between reactions (rather than dynamics of concentrations) makes their applicability potentially wide and useful beyond biological case studies. In this thesis, we construct a reaction system model corresponding to the heat shock response mechanism based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We also introduce for RS various concepts inspired by biology, e.g., mass conservation, steady state, periodicity, etc., to do model checking of the reaction systems based models. We prove that the complexity of the decision problems related to these properties varies from P to NP- and coNP-complete to PSPACE-complete. We further focus on the mass conservation relation in an RS and introduce the conservation dependency graph to capture the relation between the species and also propose an algorithm to list the conserved sets of a given reaction system.