System-level characterization of Th2 cell development and immune cell responses to ZnO and TiO2 nanoparticles

Asthma and allergy are common diseases and their prevalence is increasing. One of the hypotheses that explains this trend is exposure to inhalable chemicals such as traffi c-related air pollution. Epidemiological research supports this theory, as a correlation between environmental chemicals and allergic respiratory diseases has been found. In addition to ambient airborne particles, one may be exposed to engineered nanosized materials that are actively produced due to their favorable physico-chemical properties compared to their bulk size counterparts. On the cellular level, improper activity of T helper (Th) cells has been connected to allergic reactions. Th cells can differentiate into functionally different effector subsets, which are identifi ed according to their characteristic cytokine profi les resulting in specifi c ability to communicate with other cells. Th2 cells activate humoral immunity and stimulate eradication of extracellular pathogens. However, persistent predominance of Th2 cells is involved in a development of number of allergic diseases. The cytokine environment at the time of antigen recognition is the major factor determining the polarization of a naïve Th cell. Th2 cell differentiation is initiated by IL4, which signals via transcription factor STAT6. Although the importance of this pathway has been evaluated in the mouse studies, the signaling components involved have been largely unknown. The aim of this thesis was to identify molecules, which are under the control of IL4 and STAT6 in Th cells. This was done by using system-level analysis of STAT6 target genes at genome, mRNA and protein level resulting in identifi cation of various genes previously not connected to Th2 cell phenotype acquisition. In the study, STAT6-mediated primary and secondary target genes were dissection from each other and a detailed transcriptional kinetics of Th2 cell polarization of naïve human CD4+ T cells was collected. Integration of these data revealed the hierarchy of molecular events that mediates the differentiation towards Th2 cell phenotype. In addition, the results highlighted the importance of exploiting proteomics tools to complement the studies on STAT6 target genes identifi ed through transcriptional profi ling. In the last subproject, the effects of the exposure with ZnO and TiO2 nanoparticles was analyzed in Jurkat T cell line and in primary human monocyte-derived macrophages and dendritic cells to evaluate their toxicity and potential to cause infl ammation. Identifi cation of ZnO-derived gene expression showed that the same nanoparticles may elicit markedly distinctive responses in different cell types, thus underscoring the need for unbiased profi ling of target genes and pathways affected. The results gave additional proof that the cellular response to nanosized ZnO is due to leached Zn2+ ions. The approach used in ZnO and TiO2 nanoparticle study demonstrated the value of assessing nanoparticle responses through a toxicogenomics approach. The increased knowledge of Th2 cell signaling will hopefully reveal new therapeutic nodes and eventually improve our possibilities to prevent and tackle allergic infl ammatory diseases.

Chaotic Behaviour of Active Magnetic Bearing System by Time Series Analysis

Chaotic behaviour is one of the hardest problems that can happen in nonlinear dynamical systems with severe nonlinearities. It makes the system's responses unpredictable. It makes the system's responses to behave similar to noise. In some applications it should be avoided. One of the approaches to detect the chaotic behaviour is nding the Lyapunov exponent through examining the dynamical equation of the system. It needs a model of the system. The goal of this study is the diagnosis of chaotic behaviour by just exploring the data (signal) without using any dynamical model of the system. In this work two methods are tested on the time series data collected from AMB (Active Magnetic Bearing) system sensors. The rst method is used to nd the largest Lyapunov exponent by Rosenstein method. The second method is a 0-1 test for identifying chaotic behaviour. These two methods are used to detect if the data is chaotic. By using Rosenstein method it is needed to nd the minimum embedding dimension. To nd the minimum embedding dimension Cao method is used. Cao method does not give just the minimum embedding dimension, it also gives the order of the nonlinear dynamical equation of the system and also it shows how the system's signals are corrupted with noise. At the end of this research a test called runs test is introduced to show that the data is not excessively noisy.