THE IMPACT OF VIRUS INFECTION IN DEREGULATION OF CYTOKINE PRODUCTION UPON SECONDARY BACTERIAL INFECTION

Dendritic cells (DCs) secrete cytokines such as interleukin-23 (IL-23) when stimulated with certain Toll-like receptor (TLR) agonists and infected with pathogens such as P. aeruginosa. IL- 23 is a proinflammatory cytokine that plays a critical role in the proliferation and differentiation of the IL-17 producing Th17- CD4 T helper cells. The lack of efficient cytokine production from antigen-presenting cells, such as DCs, can impact CD4 differentiation and thus impair the immune responses against pathogens. Clearance of some bacterial infections, such as Klebsiella pneumonia and Listeria monocytogenes has been shown to be dependent on the induction of IL-23 and therefore, deregulation of these cytokines as a direct result of virus infection may impede immune responses to secondary infections. Here, an inhibition of TLR ligand or P. aeruginosa-induced IL- 23 expression in Lymphocytic Choriomeningitis Virus (LCMV)-infected bone marrow-derived dendritic cells (BMDCs) has been demonstrated, indicating that an important function of these cells is disrupted during virus/bacterial coinfection. While production of TNF-α was unaffected in LPS stimulated cells, TNF-α was significantly inhibited in bacterium infected cells by LCMV. Type I IFN in LPS or LCMV infected cell was not detected and therefore, ruling out the possibility of cytokine suppression by Type I IFN. The production of IL-10 was high in BMDCs infected with LCMV and stimulated with LPS or bacteria. Analysis of multiple cytokines produced in this coinfection model demonstrated that LCMV infection impacts specific cytokine production upon LPS or bacterium infection, which may be important for bacterial clearance. This data is important for future immunotherapy use in viral/bacterial coinfection scenarios.

β-Arrestin-mediated Regulation of the Human Ether-a-go-go-Related Gene (hERG) Potassium Channel

The human ether-a-go-go-related gene (hERG) encodes the voltage-gated K+ channel, hERG (Kv11.1). This channel passes the rapidly-activating delayed rectifier K+ current (IKr), which is important for cardiac repolarization. A reduction in IKr due to loss-of-function mutations or drug interactions causes long QT syndrome (LQTS), which can lead to cardiac arrhythmias and sudden cardiac death. The density of hERG channels in the plasma membrane is a key determinant of normal physiological function, and is balanced by trafficking to and from the cell surface. Many LQTS-associated hERG mutations result in a trafficking deficiency of otherwise functional channels. Thus, elucidating mechanisms of hERG regulation at the plasma membrane is useful for the prevention and treatment of LQTS. We previously demonstrated that M3 muscarinic receptor activation increases mature hERG expression through a Gq protein-dependent protein kinase C (PKC) pathway. In addition to conventional Gq protein-coupling, M3 receptors recruit β-arrestins upon agonist binding. Traditionally known for their role in receptor desensitization and internalization, β-arrestins also act as adaptor proteins to facilitate G protein-independent signaling. In the present work, I investigated the exclusive effect of β-arrestin signaling on hERG expression by utilizing an arrestin-biased M3 designer receptor (M3D-arr) exclusively activated by clozapine-N-oxide (CNO). By expressing M3D-arr in hERG-HEK cells and treating with CNO under various conditions, I found that M3D-arr activation increased mature hERG expression and current. Within this paradigm, M3D-arr recruited β-arrestin to the plasma membrane, and promoted the PI3K-dependent activation of Akt. I further found that the activated Akt acted through phosphatidylinositol 3-phosphate 5-kinase (PIKfyve) and Rab11 to facilitate endosomal recycling of hERG channels to the plasma membrane.

Petri Net Siphon Analysis and Network Centrality Measures for Identifying Combination Therapies in Signaling Pathways

Aberrant behavior of biological signaling pathways has been implicated in diseases such as cancers. Therapies have been developed to target proteins in these networks in the hope of curing the illness or bringing about remission. However, identifying targets for drug inhibition that exhibit good therapeutic index has proven to be challenging since signaling pathways have a large number of components and many interconnections such as feedback, crosstalk, and divergence. Unfortunately, some characteristics of these pathways such as redundancy, feedback, and drug resistance reduce the efficacy of single drug target therapy and necessitate the employment of more than one drug to target multiple nodes in the system. However, choosing multiple targets with high therapeutic index poses more challenges since the combinatorial search space could be huge. To cope with the complexity of these systems, computational tools such as ordinary differential equations have been used to successfully model some of these pathways. Regrettably, for building these models, experimentally-measured initial concentrations of the components and rates of reactions are needed which are difficult to obtain, and in very large networks, they may not be available at the moment. Fortunately, there exist other modeling tools, though not as powerful as ordinary differential equations, which do not need the rates and initial conditions to model signaling pathways. Petri net and graph theory are among these tools. In this thesis, we introduce a methodology based on Petri net siphon analysis and graph network centrality measures for identifying prospective targets for single and multiple drug therapies. In this methodology, first, potential targets are identified in the Petri net model of a signaling pathway using siphon analysis. Then, the graph-theoretic centrality measures are employed to prioritize the candidate targets. Also, an algorithm is developed to check whether the candidate targets are able to disable the intended outputs in the graph model of the system or not. We implement structural and dynamical models of ErbB1-Ras-MAPK pathways and use them to assess and evaluate this methodology. The identified drug-targets, single and multiple, correspond to clinically relevant drugs. Overall, the results suggest that this methodology, using siphons and centrality measures, shows promise in identifying and ranking drugs. Since this methodology only uses the structural information of the signaling pathways and does not need initial conditions and dynamical rates, it can be utilized in larger networks.