Microbe-induced activation of inflammatory cytokine response in human cells

Human body is in continuous contact with microbes. Although many microbes are harmless or beneficial for humans, pathogenic microbes possess a threat to wellbeing. Antimicrobial protection is provided by the immune system, which can be functionally divided into two parts, namely innate and adaptive immunity. The key players of the innate immunity are phagocytic white blood cells such as neutrophils, monocytes, macrophages and dendritic cells (DCs), which constantly monitor the blood and peripheral tissues. These cells are armed for rapid activation upon microbial contact since they express a variety of microbe-recognizing receptors. Macrophages and DCs also act as antigen presenting cells (APCs) and play an important role in the development of adaptive immunity. The development of adaptive immunity requires intimate cooperation between APCs and T lymphocytes and results in microbe-specific immune responses. Moreover, adaptive immunity generates immunological memory, which rapidly and efficiently protects the host from reinfection. Properly functioning immune system requires efficient communication between cells. Cytokines are proteins, which mediate intercellular communication together with direct cell-cell contacts. Immune cells produce inflammatory cytokines rapidly following microbial contact. Inflammatory cytokines modulate the development of local immune response by binding to cell surface receptors, which results in the activation of intracellular signalling and modulates target cell gene expression. One class of inflammatory cytokines chemokines has a major role in regulating cellular traffic. Locally produced inflammatory chemokines guide the recruitment of effector cells to the site of inflammation during microbial infection. In this study two key questions were addressed. First, the ability of pathogenic and non-pathogenic Gram-positive bacteria to activate inflammatory cytokine and chemokine production in different human APCs was compared. In these studies macrophages and DCs were stimulated with pathogenic Steptococcus pyogenes or non-pathogenic Lactobacillus rhamnosus. The second aim of this thesis work was to analyze the role of pro-inflammatory cytokines in the regulation of microbe-induced chemokine production. In these studies bacteria-stimulated macrophages and influenza A virus-infected lung epithelial cells were used as model systems. The results of this study show that although macrophages and DCs share several common antimicrobial functions, these cells have significantly distinct responses against pathogenic and non-pathogenic Gram-positive bacteria. Macrophages were activated in a nearly similar fashion by pathogenic S. pyogenes and non-pathogenic L. rhamnosus. Both bacteria induced the production of similar core set of inflammatory chemokines consisting of several CC-class chemokines and CXCL8. These chemokines attract monocytes, neutrophils, dendritic cells and T cells. Thus, the results suggest that bacteria-activated macrophages efficiently recruit other effector cells to the site of inflammation. Moreover, macrophages seem to be activated by all bacteria irrespective of their pathogenicity. DCs, in contrast, were efficiently activated only by pathogenic S. pyogenes, which induced DC maturation and production of several inflammatory cytokines and chemokines. In contrast, L. rhamnosus-stimulated DCs matured only partially and, most importantly, these cells did not produce inflammatory cytokines or chemokines. L. rhamnosus-stimulated DCs had a phenotype of "semi-mature" DCs and this type of DCs have been suggested to enhance tolerogenic adaptive immune responses. Since DCs have an essential role in the development of adaptive immune response the results suggest that, in contrast to macrophages, DCs may be able to discriminate between pathogenic and non-pathogenic bacteria and thus mount appropriate inflammatory or tolerogenic adaptive immune response depending on the microbe in question. The results of this study also show that pro-inflammatory cytokines can contribute to microbe-induced chemokine production at multiple levels. S. pyogenes-induced type I interferon (IFN) was found to enhance the production of certain inflammatory chemokines in macrophages during bacterial stimulation. Thus, bacteria-induced chemokine production is regulated by direct (microbe-induced) and indirect (pro-inflammatory cytokine-induced) mechanisms during inflammation. In epithelial cells IFN- and tumor necrosis factor- (TNF-) were found to enhance the expression of PRRs and components of cellular signal transduction machinery. Pre-treatment of epithelial cells with these cytokines prior to virus infection resulted in markedly enhanced chemokine response compared to untreated cells. In conclusion, the results obtained from this study show that pro-inflammatory cytokines can enhance microbe-induced chemokine production during microbial infection by providing a positive feedback loop. In addition, pro-inflammatory cytokines can render normally low-responding cells to high chemokine producers via enhancement of microbial detection and signal transduction.

Synthesis, Reactivity and Biological Activity of 17β-HSD1 Inhibitors Based on a Thieno[2,3-d]pryrimidin-4(3H)-one Core

Breast cancer is the most common cancer in women in Western countries. In the early stages of development most breast cancers are hormone-dependent, and estrogens, especially estradiol, have a pivotal role in their development and progression. One approach to the treatment of hormone-dependent breast cancers is to block the formation of the active estrogens by inhibiting the action of the steroid metabolising enzymes. 17beta-Hydroxysteroid dehydrogenase type 1 (17beta-HSD1) is a key enzyme in the biosynthesis of estradiol, the most potent female sex hormone. The 17beta-HSD1 enzyme catalyses the final step and converts estrone into the biologically active estradiol. Blocking 17beta-HSD1 activity with a specific enzyme inhibitor could provide a means to reduce circulating and tumour estradiol levels and thus promote tumour regression. In recent years 17beta-HSD1 has been recognised as an important drug target. Some inhibitors of 17beta-HSD1 have been reported, however, there are no inhibitors on the market nor have clinical trials been announced. The majority of known 17beta-HSD1 inhibitors are based on steroidal structures, while relatively little has been reported on non-steroidal inhibitors. As compared with 17beta-HSD1 inhibitors based on steroidal structures, non-steroidal compounds could have advantages of synthetic accessibility, drug-likeness, selectivity and non-estrogenicity. This study describes the synthesis of large group of novel 17beta-HSD1 inhibitors based on a non-steroidal thieno[2,3-d]pyrimidin-4(3H)-one core. An efficient synthesis route was developed for the lead compound and subsequently employed in the synthesis of thieno[2,3-d]pyrimidin-4(3H)-one based molecule library. The biological activities and binding of these inhibitors to 17beta-HSD1 and, finally, the quantitative structure activity relationship (QSAR) model are also reported. In this study, several potent and selective 17beta-HSD1 inhibitors without estrogenic activity were identified. This establishment of a novel class of inhibitors is a progressive achievement in 17beta-HSD1 inhibitor development. Furthermore, the 3D-QSAR model, constructed on the basis of this study, offers a powerful tool for future 17beta-HSD1 inhibitor development. As part of the fundamental science underpinning this research, the chemical reactivity of fused (di)cycloalkeno thieno[2,3-d]pyrimidin-4(3H)-ones with electrophilic reagents, i.e. Vilsmeier reagent and dimethylformamide dimethylacetal, was investigated. These findings resulted in a revision of the reaction mechanism of Vilsmeier haloformylation and further contributed to understanding the chemical reactivity of this compound class. This study revealed that the reactivity is dependent upon a stereoelectronic effect arising from different ring conformations.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.