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441	Effects of carbon nanotubes on airway epithelial cells and model lipid bilayers : proteomic and biophysical studies Li, Pin January 2014 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Carbon nanomaterials are widely produced and used in industry, medicine and scientific research. To examine the impact of exposure to nanoparticles on human health, the human airway epithelial cell line, Calu-3, was used to evaluate changes in the cellular proteome that could account for alterations in cellular function of airway epithelia after 24 h exposure to 10 μg/mL and 100 ng/mL of two common carbon nanoparticles, singleand multi-wall carbon nanotubes (SWCNT, MWCNT). After exposure to the nanoparticles, label-free quantitative mass spectrometry (LFQMS) was used to study differential protein expression. Ingenuity Pathway Analysis (IPA) was used to conduct a bioinformatics analysis of proteins identified by LFQMS. Interestingly, after exposure to a high concentration (10 μg/mL; 0.4 μg/cm2) of MWCNT or SWCNT, only 8 and 13 proteins, respectively, exhibited changes in abundance. In contrast, the abundance of hundreds of proteins was altered in response to a low concentration (100 ng/mL; 4 ng/cm2) of either CNT. Of the 281 and 282 proteins that were significantly altered in response to MWCNT or SWCNT, respectively, 231 proteins were the same. Bioinformatic analyses found that the proteins common to both kinds of nanotubes are associated with the cellular functions of cell death and survival, cell-to-cell signaling and interaction, cellular assembly and organization, cellular growth and proliferation, infectious disease, molecular transport and protein synthesis. The decrease in expression of the majority proteins suggests a general stress response to protect cells. The STRING database was used to analyze the various functional protein networks. Interestingly, some proteins like cadherin 1 (CDH1), signal transducer and activator of transcription 1 (STAT1), junction plakoglobin (JUP), and apoptosis-associated speck-like protein containing a CARD (PYCARD), appear in several functional categories and tend to be in the center of the networks. This central positioning suggests they may play important roles in multiple cellular functions and activities that are altered in response to carbon nanotube exposure. To examine the effect of nanotubes on the plasma membrane, we investigated the interaction of short purified MWCNT with model lipid membranes using a planar bilayer workstation. Bilayer lipid membranes were synthesized using neutral 1, 2-diphytanoylsn-glycero-3-phosphocholine (DPhPC) in 1 M KCl. The ion channel model protein, Gramicidin A (gA), was incorporated into the bilayers and used to measure the effect of MWCNT on ion transport. The opening and closing of ion channels, amplitude of current, and open probability and lifetime of ion channels were measured and analyzed by Clampfit. The presence of an intermediate concentration of MWCNT (2 μg/ml) could be related to a statistically significant decrease of the open probability and lifetime of gA channels. The proteomic studies revealed changes in response to CNT exposure. An analysis of the changes using multiple databases revealed alterations in pathways, which were consistent with the physiological changes that were observed in cultured cells exposed to very low concentrations of CNT. The physiological changes included the break down of the barrier function and the inhibition of the mucocillary clearance, both of which could increase the risk of CNT’s toxicity to human health. The biophysical studies indicate MWCNTs have an effect on single channel kinetics of Gramicidin A model cation channel. These changes are consistent with the inhibitory effect of nanoparticles on hormone stimulated transepithelial ion flux, but additional experiments will be necessary to substantiate this correlation. Bilayer lipid membranes -- Biotechnology Nanostructured materials industry Organic compounds -- Synthesis Carbon -- Research -- Toxicology Airway (Medicine) -- Toxicology Fullerenes Proteins -- Analysis -- Data processing Bioinformatics Respiratory mucosa -- Pathophysiology Proteomics Biological transport -- Regulation Oxidative stress -- Physiological effect Cellular signal transduction Cell interaction Biology -- Research -- Data processing Cells -- Permeability Ion channels -- Research Pulmonary toxicology
442	Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies Althagafi, Azza Th. 20 July 2023 (has links) Whole-exome and genome sequencing are widely used to diagnose individual patients. However, despite its success, this approach leaves many patients undiagnosed. This could be due to the need to discover more disease genes and variants or because disease phenotypes are novel and arise from a combination of variants of multiple known genes related to the disease. Recent rapid increases in available genomic, biomedical, and phenotypic data enable computational analyses, reducing the search space for disease-causing genes or variants and facilitating the prediction of causal variants. Therefore, artificial intelligence, data mining, machine learning, and deep learning are essential tools that have been used to identify biological interactions, including protein-protein interactions, gene-disease predictions, and variant--disease associations. Predicting these biological associations is a critical step in diagnosing patients with rare or complex diseases. In recent years, computational methods have emerged to improve gene-disease prioritization by incorporating phenotype information. These methods evaluate a patient's phenotype against a database of gene-phenotype associations to identify the closest match. However, inadequate knowledge of phenotypes linked with specific genes in humans and model organisms limits the effectiveness of the prediction. Information about gene product functions and anatomical locations of gene expression is accessible for many genes and can be associated with phenotypes through ontologies and machine-learning models. Incorporating this information can enhance gene-disease prioritization methods and more accurately identify potential disease-causing genes. This dissertation aims to address key limitations in gene-disease prediction and variant prioritization by developing computational methods that systematically relate human phenotypes that arise as a consequence of the loss or change of gene function to gene functions and anatomical and cellular locations of activity. To achieve this objective, this work focuses on crucial problems in the causative variant prioritization pipeline and presents novel computational methods that significantly improve prediction performance by leveraging large background knowledge data and integrating multiple techniques. Therefore, this dissertation presents novel approaches that utilize graph-based machine-learning techniques to leverage biomedical ontologies and linked biological data as background knowledge graphs. The methods employ representation learning with knowledge graphs and introduce generic models that address computational problems in gene-disease associations and variant prioritization. I demonstrate that my approach is capable of compensating for incomplete information in public databases and efficiently integrating with other biomedical data for similar prediction tasks. Moreover, my methods outperform other relevant approaches that rely on manually crafted features and laborious pre-processing. I systematically evaluate our methods and illustrate their potential applications for data analytics in biomedicine. Finally, I demonstrate how our prediction tools can be used in the clinic to assist geneticists in decision-making. In summary, this dissertation contributes to the development of more effective methods for predicting disease-causing variants and advancing precision medicine. Whole-Exome Sequencing Whole-Genome Sequencing Disease Genes Disease Variants Disease Phenotypes Causal Variants Prediction Causal Genes Prediction Artificial Intelligence Data Mining Machine Learning Deep Learning Data Analytics Biological Interactions Protein-Protein Interactions Gene-Disease Predictions Variant-Disease Associations Rare Diseases Complex Diseases Gene-Phenotype Associations Ontology Gene Product Functions Anatomical Locations Gene Prioritization Variant Prioritization Loss of Gene Function Background Knowledge Data Biological Knowledge Graph Graph-Based Machine Learning Biomedical Ontologies Linked Biological Data Representation Learning Embeddings Data Integration Precision Medicine Decision-Making Biomedicine.
443	Structural Investigation of Processing α-Glucosidase I from Saccharomyces cerevisiae Barker, Megan 20 August 2012 (has links) N-glycosylation is the most common eukaryotic post-translational modification, impacting on protein stability, folding, and protein-protein interactions. More broadly, N-glycans play biological roles in reaction kinetics modulation, intracellular protein trafficking, and cell-cell communications. The machinery responsible for the initial stages of N-glycan assembly and processing is found on the membrane of the endoplasmic reticulum. Following N-glycan transfer to a nascent glycoprotein, the enzyme Processing α-Glucosidase I (GluI) catalyzes the selective removal of the terminal glucose residue. GluI is a highly substrate-specific enzyme, requiring a minimum glucotriose for catalysis; this glycan is uniquely found in biology in this pathway. The structural basis of the high substrate selectivity and the details of the mechanism of hydrolysis of this reaction have not been characterized. Understanding the structural foundation of this unique relationship forms the major aim of this work. To approach this goal, the S. cerevisiae homolog soluble protein, Cwht1p, was investigated. Cwht1p was expressed and purified in the methyltrophic yeast P. pastoris, improving protein yield to be sufficient for crystallization screens. From Cwht1p crystals, the structure was solved using mercury SAD phasing at a resolution of 2 Å, and two catalytic residues were proposed based upon structural similarity with characterized enzymes. Subsequently, computational methods using a glucotriose ligand were applied to predict the mode of substrate binding. From these results, a proposed model of substrate binding has been formulated, which may be conserved in eukaryotic GluI homologs. Biomolecular structure Eukaryotic glycosylation glycosylation N-glycan N-linked glycosylation Carbohydrate biochemistry Inhibitor antiviral therapeutic Structure-based drug design glucosidase glycosidase glycoside hydrolase post-translational modification enzyme enzyme mechanism inverting mechanism structural biology protein structure 6xHis tag Crystal packing sugar substrate X-ray crystallography single anomalous dispersion PHENIX heavy-atom phasing docking autodock autodock vina biophysical methods tryptophan fluorescence glucose oxidase activity assay enzyme inhibition Protein expression Protein purification Pichia pastoris Saccharomyces cerevisiae fondue AOX1 promoter glycoside hydrolysis substrate binding model molecular dynamics molecular dynamics Binding site Active site cleft Endoplasmic reticulum Glycan processing Glycan trimming protein-protein interactions cell-cell communication X-ray diffraction crystallization secreted protein substrate selectivity kojibiose glucotriose miglitol deoxynojirimycin Glc3Man9GlcNAc2 free oligosaccharide species GluI Cwh41 Cwh41p Cwht1p 0306 0307 0760
444	Structural Investigation of Processing α-Glucosidase I from Saccharomyces cerevisiae Barker, Megan 20 August 2012 (has links) N-glycosylation is the most common eukaryotic post-translational modification, impacting on protein stability, folding, and protein-protein interactions. More broadly, N-glycans play biological roles in reaction kinetics modulation, intracellular protein trafficking, and cell-cell communications. The machinery responsible for the initial stages of N-glycan assembly and processing is found on the membrane of the endoplasmic reticulum. Following N-glycan transfer to a nascent glycoprotein, the enzyme Processing α-Glucosidase I (GluI) catalyzes the selective removal of the terminal glucose residue. GluI is a highly substrate-specific enzyme, requiring a minimum glucotriose for catalysis; this glycan is uniquely found in biology in this pathway. The structural basis of the high substrate selectivity and the details of the mechanism of hydrolysis of this reaction have not been characterized. Understanding the structural foundation of this unique relationship forms the major aim of this work. To approach this goal, the S. cerevisiae homolog soluble protein, Cwht1p, was investigated. Cwht1p was expressed and purified in the methyltrophic yeast P. pastoris, improving protein yield to be sufficient for crystallization screens. From Cwht1p crystals, the structure was solved using mercury SAD phasing at a resolution of 2 Å, and two catalytic residues were proposed based upon structural similarity with characterized enzymes. Subsequently, computational methods using a glucotriose ligand were applied to predict the mode of substrate binding. From these results, a proposed model of substrate binding has been formulated, which may be conserved in eukaryotic GluI homologs. Biomolecular structure Eukaryotic glycosylation glycosylation N-glycan N-linked glycosylation Carbohydrate biochemistry Inhibitor antiviral therapeutic Structure-based drug design glucosidase glycosidase glycoside hydrolase post-translational modification enzyme enzyme mechanism inverting mechanism structural biology protein structure 6xHis tag Crystal packing sugar substrate X-ray crystallography single anomalous dispersion PHENIX heavy-atom phasing docking autodock autodock vina biophysical methods tryptophan fluorescence glucose oxidase activity assay enzyme inhibition Protein expression Protein purification Pichia pastoris Saccharomyces cerevisiae fondue AOX1 promoter glycoside hydrolysis substrate binding model molecular dynamics molecular dynamics Binding site Active site cleft Endoplasmic reticulum Glycan processing Glycan trimming protein-protein interactions cell-cell communication X-ray diffraction crystallization secreted protein substrate selectivity kojibiose glucotriose miglitol deoxynojirimycin Glc3Man9GlcNAc2 free oligosaccharide species GluI Cwh41 Cwh41p Cwht1p 0306 0307 0760
445	Electrostaticanalisys the Ras active site Khan, Abdul Kareem 05 March 2009 (has links) La preorganització electrostàtica del centre actiu s'ha postulat com el mecanisme genèric de l'acció dels enzims. Així, alguns residus "estratègics" es disposarien per catalitzar reaccions interaccionant en una forma més forta amb l'estat de transició, baixant d'aquesta manera el valor de l'energia dactivació g cat. S'ha proposat que aquesta preorientació electrostática s'hauria de poder mostrar analitzant l'estabilitat electrostàtica de residus individuals en el centre actiu.Ras es una proteïna essencial de senyalització i actúa com un interruptor cel.lular. Les característiques estructurals de Ras en el seu estat actiu (ON) són diferents de les que té a l'estat inactiu (OFF). En aquesta tesi es duu a terme una anàlisi exhaustiva de l'estabilitat dels residus del centre actiu deRas en l'estat actiu i inactiu. / The electrostatic preorganization of the active site has been put forward as the general framework of action of enzymes. Thus, enzymes would position "strategic" residues in such a way to be prepared to catalyze reactions byinteracting in a stronger way with the transition state, in this way decreasing the activation energy g cat for the catalytic process. It has been proposed thatsuch electrostatic preorientation should be shown by analyzing the electrostatic stability of individual residues in the active site.Ras protein is an essential signaling molecule and functions as a switch in thecell. The structural features of the Ras protein in its active state (ON state) are different than those in its inactive state (OFF state). In this thesis, an exhaustive analysis of the stability of residues in the active and inactive Ras active site is performed. dinàmica centre actiu relacions d'estructura activitat preorganització reorganització estat de transició estat actiu estabilitat electrostàtica interacción proteïna-proteïna test de ranks de Wilcoxon P-loop G1 motif HVR metastability RMSD substrate assisted catalysis sequence alignment beta sheet helix Z-test wilcoxon rank test protein-protein interactions electrostatic stability active state dynamics signal transduction electrostatic stabilization ras effectors guanine exchange factor GTPase-activating proteins quantum mechanics/molecular PDLD/S-LRA X-ray crystallography solvation energy free energy perturbation statistical analysis GTP hydrolysis guanosine diphosphate guanosine triphosphate computer simulation thermodynamics protein conformation electrostatics interruptor II interruptor I llaç P motiu G1 HVR (regions altament variables) metaestabilita RMSD catàlisi assistida pel substrat aliniament de seqüència fulla beta hèlix test Z transducció del senyal estabilització electrostàtica efectors de ras factor de bescamvi de guanina proteïnes activadores de l'activitat mecànica quàntica/mecànica PDLD/S-LRA cristalografia de raigs X energia de solvatació perturbació de l'energia lliure anàlisi estadística hidròlisi de GTP difosfat de guanosina trifosfat de guanosina simulació computacional termodinàmica conformació proteïca electrostàtica switch-I switch-II 537 576

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