The connexins are a family of 21 proteins that represent the structural units of intercellular gap junctions and single membrane hemichannels. These channels provide a means for cells to exchange small metabolites and signaling molecules with adjacent cells and the extracellular space, respectively. Compelling evidence implicates connexins, and the more recently discovered pannexins, in the control of neural progenitor cell proliferation, survival and migration. Moreover, connexin and pannexin dysregulation following central nervous system injuries such as cerebral ischemia, spinal cord injury, and epilepsy contributes to the secondary expansion of lesions days and weeks after the initial insult. While these data suggest that connexins and pannexins represent novel therapeutic targets to both reduce the extent of neural injury and promote neural repair and regeneration, we currently lack the necessary repertoire of therapeutically useful connexin- and pannexin-specific compounds to test these hypotheses. In this thesis, I conducted targeted screening of a large, ethnobotanically-derived library to address my overarching objective of identifying compounds that selectively alter connexin and/or pannexin channel function. To accomplish this, I characterized the repertoire of connexins and pannexins expressed by neural progenitor cell-like NT2/D1 cells, quantified the intercellular flux of calcein through connexin gap junctions, and measured the uptake of lucifer yellow and propidium iodide through pannexin hemichannels. Collectively, these screens identified several promising lead compounds and ethanolic plant extracts that selectively alter connexin and pannexin channel activity.

According to the World Health Organization, malaria has been classified as one of the three most important infectious diseases in Africa. The number of malaria cases is still on the increase in various countries, such as Rwanda and Zambia, which highlights the fragility of malaria control and the need to maintain and improve control programs. An innovative strategy for developing new antimalarial agents is through targeting epigenetic regulatory mechanisms in the malarial parasite, Plasmodium falciparum. Histone posttranslational modifications (PTMs) are factors contributing to epigenetic regulation in P. falciparum parasites. The epigenetic regulatory enzyme, Lysine-specific demethylase 1 (LSD1), has the ability to remove methyl groups from mono- and dimethylated lysine residues and is a regulator of gene expression through the modulation of chromatin structure. Polyamine analogues have been described as epi-drugs that target cell cycle development by blocking epigenetic control mechanisms in mammalian cells. A library of polyamine analogues were tested in cancer cells and found to specifically inhibit LSD1. In addition, these analogues were shown to have antiplasmodial activity against a drug-sensitive parasite strain, with IC50 values ranging from 88-100 nM but were metabolically unstable in vivo. In an attempt to overcome this in vivo hurdle, the leading compound was fluorinated at four different positions and tested for improved antiplasmodial activity and selectivity towards the parasites. Furthermore, the effect of the compounds on epigenetic regulatory mechanisms, through inhibition of LSD1 activity, was investigated. The analogues showed inhibition of parasite proliferation at low nanomolar concentrations and were very selective towards the parasites with low resistance indices. The leading compound showed reversible cytotoxicity towards parasite proliferation in addition to inhibitory activity against LSD1 and therefore, epigenetic regulatory changes. The approach taken in this dissertation is novel as none of the currently available antimalarials target LSD1 and as such, adds valuable information to future perspectives for drug design. / Dissertation (MSc)--University of Pretoria, 2015. / tm2015 / Biochemistry / MSc / Unrestricted

No description available.

Software-De_ned Networking (SDN) proposes a new network architecture inwhich the control plane and forwarding plane are decoupled. SDN can improvenetwork e_ciency and ease of management through the centralization of the controland policy decisions. However, SDN deployments are currently limited todata-center and experimental environments. This thesis surveys the deploymentof SDN from the perspective of a telecommunication network operator. We discussthe strategies which enable the operator to migrate to a network in whichboth SDN and legacy devices interoperate. As a synthesis of existing technologiesand protocols, we formulate an automated process for the bootstrapping of newlydeployed forwarding devices. Furthermore, we review solutions for programmingthe forwarding devices and for performing topology discovery. The functionalcorrectness of the proposed bootstrapping process is evaluated in an emulatedenvironment.

Candida albicans is a fungus found in the normal biota of humans, but in immuno-compromised individuals, C. albicans forms complex biofilms on the surface of medical prosthetics, skin, oral cavities, the urinary tract, and other epithelial cell layers. Biofilms and the development of drug resistance has limited treatment options. Antimicrobial peptides (AMPs) are increasingly becoming attractive therapeutic agents for the treatment of these infections due to their multifunctional properties, multiple cellular targets, and the lower incidence of resistance development. Previous studies have shown that Os, an AMP derived from the tick defensin OsDef2, has antifungal activity against C. albicans. Preliminary antifungal mode of action studies indicated that Os induces the formation of reactive oxygen species although not a primary mode of killing. Os causes membrane permeabilization, which is inhibited by an excess of free laminarin and mannan. Furthermore, Os was shown to bind plasmid DNA but was inactive in high salt conditions. The aim of this study was to further explore the mode of action of Os in planktonic C. albicans (ATCC 90028) cells. A modified microbroth dilution assay was developed to allow rapid screening of salt sensitive AMPs such as Os. With this method the IC50 of the positive control, amphotericin B (AmpB), and Os were determined as 0.547 ± 0.056 μM and 1.163 ± 0.116 μM, respectively. The effects of AmpB and Os on cellular morphology were evaluated using scanning electron microscopy and transmission electron microscopy at their respective IC25, IC50 and IC75 values. When comparing the effects of Os with AmpB on the cell wall and membrane, Os had more severe and nonspecific effects. Os induced the formation of pits on the cell surface and pores in the cell membrane, as well as increased budding scars. Using isothermal titration calorimetry, no interaction between Os and the fungal cell wall components, mannan and laminarin, could be detected. Factors such as the lack of tryptophan and aspartate residues as well as β-sheet secondary structures may account for the lack of interaction. However, with the modified microbroth dilution assay in the presence of excess of mannan or laminarin (20 mg/mL), reduced activity from Os was observed. The formation of soluble macro-complexes between Os and the cell wall components at high concentrations may account for reduced activity. The ability of Os to cause membrane depolarization was evaluated with bis-(1,3-dibutylbarbituric acid) trimethine oxonol. The control, melittin, caused a linear increase in depolarization with a significant increase at 0.63 μM, while Os caused a sigmoidal increase in depolarization with a significant increase at 2.5 μM. Therefore, membrane depolarization occurs following membrane permeabilization which occurs at 2 μM. Finally, the localisation of 0.5 μM and 6.4 μM (IC25, IC75) 5-FAM-Os, and concurrently the effect on vacuoles loaded with CellTracker Blue-CMAC, was determined with flow cytometry and confocal laser scanning microscopy (CLSM). Findings were that Os, at a concentration below its IC50, binds to the cell membrane, then translocates and binds DNA. At a concentration above its IC50, Os accumulates in the cytoplasm and causes destruction of membranes, including that of vacuoles, leading to cell death. In conclusion, this study shows that Os is a membrane acting AMP that can be further developed for clinical application as an antifungal drug. / Dissertation (MSc (Biochemistry))--University of Pretoria, 2020 / NRF / Biochemistry / MSc (Biochemistry) / Unrestricted

The main focus of our work is to develop, apply and assess cheminformatics tools and methods. In particular, we focus on the following three areas: Integration of open source tools with application to drug discovery, usability studies to assess the efficacy of these software tools and finally, developing novel techniques for database query. Rapid globalization in the present time has sparked a need in the scientific community to interact with each other at an economic and a fast pace. This is achieved by developing and sharing open source databases using World Wide Web. A web based open source database application has been developed to incorporate freeware from varied sources. The deployment of developed database and user interface in a university lab setting is discussed. To aid in connecting the end user and the software tools, usability studies are necessary. These studies communicate the end users’ needs and desires, resulting in a user-friendly and more powerful interactive software packages. Usability studies were conducted on developed database student application and on different drawing packages to determine their effectiveness. Developing new and interactive search engines to query publicly available databases helps researchers work more efficiently. The huge volume of data available and its heterogeneous nature presents issues related to querying, integration and presentation. In aiding the retrieval process, an innovative multi faceted classification system, called ChemFacets, is developed. This system provides dynamic categorization of large result sets retrieved from multiple databases.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20002号 / 工博第4246号 / 新制||工||1657(附属図書館) / 33098 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 濵地 格, 教授 森 泰生, 教授 跡見 晴幸 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Salmonella enterica serovar Typhimurium (Salmonella) is a Gram-negative bacterial pathogen capable of causing both gastroenteritis and bacteraemia in human hosts. During infection, Salmonella invokes a complex network of virulence factors, regulatory systems, and metabolic pathways to promote immune evasion, sometimes demanding antibiotic treatment for resolution. Unfortunately, antibiotic resistance has reached critical levels in this and other pathogens, necessitating the discovery of new anti-infective targets and treatment options. Herein, we have sought to exploit the dynamic interactions between Salmonella and the host immune system to identify new, conditionally active anti-Salmonella therapies. In chapter 2, we aim to identify chemical compounds that are selectively antimicrobial against intracellular Salmonella, and discover that the anxiolytic drug metergoline inhibits Salmonella survival in cultured macrophages and systemically infected mice. In chapter 3, we screen for anti-virulence compounds that target regulatory signaling in Salmonella, and characterize the inhibitory activity of methyl-3,4-dephostatin, which perturbs SsrA/B and PmrB/A signaling and enhances sensitivity to colistin in vitro and in vivo. In chapter 4, we identify several host-directed compounds that modulate macrophage immunity and investigate their ability to attenuate a multidrug resistant Salmonella infection. Together, the work presented in this thesis demonstrates the potential for drug screening in infection-relevant conditions to identify new anti-infectives with non-traditional targets. / Thesis / Doctor of Philosophy (PhD)

Atomistic modeling and simulation methods facilitate biomedical research from many respects, including structure-based drug design. The ability of these methods to address biologically relevant problems is largely determined by the accuracy of the treatment of complex solvation effects in target biomolecules surrounded by water. The implicit solvent model – which treats solvent as a continuum with the dielectric and non-polar properties of water – offers a good balance between accuracy and speed. Simple and efficient, generalized Born (GB) model has become a widely used implicit solvent responsible for the estimation of key electrostatic interactions. The main goal of this research is to improve the accuracy of protein-ligand binding calculations in the implicit solvent framework. To address the problem (1) GBNSR6, an accurate yet efficient flavor of GB, has been thoroughly explored in the context of protein-ligand binding, (2) a global multidimensional optimization pipeline is developed to find the optimal dielectric boundary made of atomic and water probe radii specifically for protein-ligand binding calculations using GBNSR6. The pipeline includes (3) two novel post-processing steps for optimum robustness analysis and optimization landscape visualization. In the final step of this research, (4) accuracy gain the optimal dielectric boundary can bring in practice is explored on binding benchmarks, including the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. / Doctor of Philosophy / Drug discovery is one of the most challenging tasks in biological sciences as it takes about 10-15 years and $1.5-2 billion on average to discover a new drug. Therefore, efforts to speed up this process or lower its costs are highly valuable. Computer-aided drug design (CADD) plays a crucial role in the early stage of drug discovery. In CADD, computational approaches are used in order to discover, develop, and analyze drugs and similar biologically active molecules, such as proteins. Proteins are an important class of biological macromolecules that perform their functionality mainly through interactions with other molecules, for example, binding to small molecules so-called ligands. Thorough understanding of protein-ligand interactions is central to comprehending biology at the molecular level. In this study, we introduce and analyze a computational model used for protein-ligand binding free energy calculations. A global multidimensional optimization pipeline is developed to find the optimal parameters of the model,˘aparticularly˘athose parameters involved in the dielectric boundary. In order to examine the robustness of the optimal model to unavoidable perturbations and uncertainties, virtually inevitable in any complex system being optimized, a novel robustness metric is introduced. Finally, the robust optimal model is tested on protein-ligand benchmarks, including a complex related to the novel coronavirus. Results demonstrate relatively higher accuracy in terms of binding free energy calculations compared to reference models.

No / Objectives The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. Methods and materials This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. Results and conclusions The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.