No description available.

Alteration of serum tryptophan leads to parallel alterations in brain tryptophan levels. Such changes in brain tryptophan levels has been shown to lead to mood disturbances. The primary enzyme responsible for altering serum tryptophan levels is the liver cytosolic enzyme, tryptophan pyrrolase. Activation of this enzyme is responsible for the enhanced catabolism of circulating tryptophan. The purpose of the present study was firstly to establish whether there is a link between sex steroids and tryptophan pyrrolase activity especially since sex steroids are also known to cause mood disturbances and secondly to determine the effects of sex steroids on brain indolamine metabolism. The results show that all three sex steroids induce the activity of tryptophan pyrrolase implying that they decrease serum tryptophan levels by the activation of tryptophan pyrrolase, thus making less tryptophan available for uptake by the brain. It was also shown that the sex steroids enhance the uptake of ¹⁴C-tryptophan by brain synatopsomes. In addition, the sex steroids influenced the pattern of metabolism of serotonin by organ cultures of rat pineal glands. It is possible that the sex steroids regulate the availability and uptake of indoleamines in the brain.

published_or_final_version / Community Medicine / Master / Master of Public Health

Venoms are complex mixtures of biological macromolecules and other compounds that are used for predatory and defensive purposes by hundreds of thousands of known species worldwide. Throughout human history, venoms and venom components have been used to treat a vast array of illnesses, causing them to be of great clinical, economic, and academic interest to the drug discovery and toxinology communities. In spite of major computational advances that facilitate data-driven drug discovery, most therapeutic venom effects are still discovered via tedious trial-and-error, or simply by accident. In this dissertation, I describe a body of work that aims to establish a new subdiscipline of translational bioinformatics, which I name “computational toxinology”. To accomplish this goal, I present three integrated components that span a wide range of informatics techniques: (1) VenomKB, (2) VenomSeq, and (3) VenomKB’s Semantic API. To provide a platform for structuring, representing, retrieving, and integrating venom data relevant to drug discovery, VenomKB provides a database-backed web application and knowledge base for computational toxinology. VenomKB is structured according to a fully-featured ontology of venoms, and provides data aggregated from many popular web re- sources. VenomSeq is a biotechnology workflow that is designed to generate new high-throughput sequencing data for incorporation into VenomKB. Specifically, we expose human cells to controlled doses of crude venoms, conduct RNA-Sequencing, and build profiles of differential gene expression, which we then compare to publicly-available differential expression data for known dis- eases and drugs with known effects, and use those comparisons to hypothesize ways that the venoms could act in a therapeutic manner, as well. These data are then integrated into VenomKB, where they can be effectively retrieved and evaluated using existing data and known therapeutic associations. VenomKB’s Semantic API further develops this functionality by providing an intelligent, powerful, and user-friendly interface for querying the complex underlying data in VenomKB in a way that reflects the intuitive, human-understandable mean- ing of those data. The Semantic API is designed to cater to the needs of advanced users as well as laypersons and bench scientists without previous expertise in computational biology and semantic data analysis. In each chapter of the dissertation, I describe how we evaluated these 3 components through various approaches. We demonstrate the utility of VenomKB and the Semantic API by testing a number of practical use-cases for each, designed to highlight their ability to rediscover existing knowledge as well as suggesting potential areas for future exploration. We use statistics and data science techniques to evaluate VenomSeq on 25 diverse species of venomous animals, and propose biologically feasible explanations for significant findings. In evaluating the Semantic API, I show how observations on VenomSeq data can be interpreted and placed into the context of past research by members of the larger toxinology community. Computational toxinology is a toolbox designed to be used by multiple stakeholders (toxinologists, computational biologists, and systems pharmacologists, among others) to improve the return rate of clinically-significant findings from manual experimentation. It aims to achieve this goal by enabling access to data, providing means for easy validation of results, and suggesting specific hypotheses that are preliminarily supported by rigorous inferential statistics. All components of the research I describe are open-access and publicly available, to improve reproducibility and encourage widespread adoption

Colchicine and paclitaxel are two common drugs used in chemotherapy to halt tumor growth. In the present study IAR 20 cells were treated for 24 and 48 hr with colchicine and paclitaxel alone, in combination or no drug which served as a control. Through the use of Western blotting, we determined that the treatments affected expression due of several proteins including bcl-2, bax, p53 and caspase-8. The changes observed in protein expression due to the treatments correlated to the photomicrographs of the cells in culture and cell viability, indicating that the drugs were activating and initiating apoptosis. Interestingly, morphological changes such as membrane blebbing and cell swelling (indicators of apoptosis) were observed in the treated cultures and even more important the combined treatment yielded both changes in morphology. Also, activity assays were performed to study the effects the treatments had on the activities of SAPK/JNK and caspase-3, known activators of apoptosis. High activities of SAPK/JNK and caspase-3 in 48 hr treatments directly influenced cell viability in that the treatments with the highest activities yielded the lowest cell numbers, indicating that apoptosis was occurring. Based on these findings it was concluded that combined treatments of colchicine and paclitaxel are not advantageous in hepatocytes and could provide some insight into the treatment of liver cancer. Additionally, it appeared the drugs were initiating apoptosis in a p53 independent manner. / Department of Biology

Computational methods provide important insights in structural features and properties of many systems, which in turn, reduce the cost of drug discovery. Accurate calculations of electrochemical properties and pKa predictions are crucial for understanding and modeling of many chemical reactions and biological processes. This dissertation will present two different classes of computational methods for the calculation of electrochemical properties and pKa values in two different systems. In the first part, we demonstrate the pKa calculations of histidine residues in proteins by Free Energy Perturbation (FEP) and evaluate several protein pKa prediction methods. In the second part, we demonstrate the Density Functional Theory d-block localized orbital correction (DFT-DBLOC) methodology in calculating of redox potentials and spin state splittings for octahedral transition metal containing species. Ionizable side chains in proteins are involved in catalysis and play a key role in the pH-dependence of a variety of biological reactions. The ability to understand and model these effects requires an accurate pKa prediction of ionizable residues. The correct assignment of protonation state at a given pH helps to determine properties including protein solubility, protein folding, catalytic activity and protein–ligand binding affinities. Several computational methods have been developed to predict the residue pKa based on protein structure. Although some methods produced accurate predictions within 1 pKa unit RMS error, the RMSE over a large data set is not necessarily a good predictor of accuracy for specific types of protein environments. Most datasets studied in the pKa predictions contain highly solvent exposed residues which exhibit minimal perturbations from the intrinsic pKa values in solution. As the fraction of exposure to the solvent of the residue decreases, the predictive power of methods diminishes. However, these buried residues are often the most important residues from the standpoint of binding, catalytic activity, and other biologically important functions. We have applied Free Energy Perturbation (FEP) method to predict a large dataset of experimentally measured pKa values of histidines in proteins and compare the results to experimental data. Histidines are particularly crucial because the imidazole side chain of histidine can serve as both acids and bases near physiological pH values and as both hydrogen bond donors and acceptors. We explain the factors determining pKa values and improve pKa predictions using enhanced protocols. We demonstrate improved performance using the FEP methodology vs example empirical and continuum solvent-based methodologies. In Chapter 4, we have evaluated the performance of the M06 and PBE0 DFT functionals and the DFT-DBLOC methodology in their ability to calculate spin splittings and redox potentials for octahedral complexes containing a first-row transition metal series atom. These quantities play a critical role in a wide range of transition metal chemistry and physics, including catalysis, electron transfer, and conductivity. The mean unsigned errors (MUEs) for these two functionals are similar to those obtained for B3LYP using the same data sets. We then apply our localized orbital correction approach for transition metals, DBLOC, in an effort to improve the results obtained with both functionals. The PBE0- DBLOC results are remarkably close in both MUE and parameter values to those obtained for the B3LYP-DBLOC method. The M06-DBLOC results are less accurate, but the parameter values and trends are still qualitatively very similar. These results demonstrate that DBLOC corrected methods are substantially more accurate for these systems than any of the uncorrected functionals we have tested and that the deviations between hybrid DFT methods and experiment for transition metal containing systems exhibit striking physically based regularities which are very similar for the three functionals that we have examined, despite significant differences in the details of each model.

Wang, Zexi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 63-64). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 0 --- Introduction --- p.1 / Chapter 1 --- The Design of the Experiments --- p.4 / Chapter 1.1 --- Flies and the Devices --- p.5 / Chapter 1.2 --- Parameter Settings and Interested Information --- p.8 / Chapter 2 --- Video Processing --- p.11 / Chapter 2.1 --- "Videos, Computer Vision and Image Processing" --- p.11 / Chapter 2.2 --- Details in Video Processing --- p.14 / Chapter 3 --- Data Analysis --- p.20 / Chapter 3.1 --- Background --- p.20 / Chapter 3.2 --- Outline of Data Analysis in Our Project --- p.22 / Chapter 4 --- Effect of the Medicine --- p.25 / Chapter 4.1 --- Hypothesis Testing --- p.26 / Chapter 4.2 --- Two-sample t Test --- p.28 / Chapter 5 --- Significance of the Two Factors --- p.32 / Chapter 5.1 --- Background of ANOVA --- p.33 / Chapter 5.2 --- The Model of ANOVA --- p.35 / Chapter 5.3 --- Two-way ANOVA in Our Data Analysis --- p.42 / Chapter 6 --- Regression Model --- p.45 / Chapter 6.1 --- Background of Regression Analysis --- p.47 / Chapter 6.2 --- Polynomial Regression Models --- p.52 / Chapter 6.2.1 --- Background --- p.52 / Chapter 6.2.2 --- R2 and adjusted R2 --- p.53 / Chapter 6.3 --- Model Verification --- p.58 / Chapter 6.4 --- A Simpler Model As the Other Choice --- p.59 / Chapter 6.5 --- Conclusions --- p.60 / Chapter 7 --- Further Studies --- p.61 / Bibliography --- p.62

No description available.

Thesis (doctoral)--Universität Heidelberg, 2002. / Includes bibliographical references.

Excitation-contraction (EC) coupling is the process by which Ca²⁺ is released from the sarcoplasmic reticulum (SR) and is fundamental to cardiac and skeletal muscle function. The SR contains many uncharacterised ion channels and proteins which may influence EC coupling and in this thesis I have investigated the biophysical properties of some of these channels. I have demonstrated that the single-channel gating and conducting properties of SR K⁺ channels from various mammalian species (rabbit, sheep and mouse) are very similar. I investigated the actions of possible physiological regulators of these channels and demonstrated that luminal Ca²⁺ and Mg²⁺ can block K⁺ flux in a voltage-dependent manner, while luminal Ca²⁺, Ni²⁺, and alkaline pH can reduce Po by additional mechanisms. I also characterised the single-channel properties of the various SR anion channels that are observed after incorporating mammalian SR vesicles into artificial membranes. The trimeric intracellular cation channels (TRIC-A and TRIC-B) and Mitsugumin 23 (MG23) are suggested to be SR cation channels. I have therefore utilised Tric-a KO and Mg23 KO mice to study SR membranes devoid of TRIC-A and MG23. Additionally, I have begun to investigate the single-channel properties of purified c. elegans TRIC-B1 and human TRIC-A. I found that SR K⁺ channel function was altered in SR from Tric-a KO or Mg23 KO tissue, however the underlying mechanisms for the observed changes appear to be complex. My initial studies of the purified TRIC-A and TRIC-B proteins show that they are permeable to K⁺, Ca²⁺, choline, and Cl^-, properties which deviate from those of SR K⁺ channels from rabbit, mouse and sheep. This may reflect species differences or alterations to protein function caused during the purification process or that SR K⁺ channels remain an unidentified class of ion channel.