FINAL RHP Dissertation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
See Also Figure 1
Figure S1. Box-and-whisker plots depicting the range of expression values per developmental stage, with DESeq normalization (A) or quantile normalization (B). See also Figure 1. Figure S2. Lv-Setmar expression has low variation over developmental time. A. A plot of Lv-setmar versus Lv-ubiquitin expression over time demonstrates that Lv-setmar exhibits less temporal variation than Lv-ubiquitin. B. A representative gel showing Lv-setmar qPCR products amplified from cDNAs representing each sequenced stage in this study, demonstrating comparable product levels and an absence of spurious amplification products. See also Figure 1E. Figure S3. LvEDGE database. Screen shots showing the home page (A), the search window (B), an example search with a temporal expression plot (C), and the numerical data reflected in the plot (D) for the LvEDGE public database, which hosts the data described herein. stage 1 2 3 4 5 6 7 8 9 10 11 Category Subcategory 2-cell 60-cell EB HB TVP MB EG MG LG EP LP meiotic Cell Division Cytokinesis Mitosis checkpoint cell division recombination cell cycle stem cell left-right cell left-right Development maintenance asymmetry morphogenesis asymmetry regulation of multicellular organismal process cell soma cell soma Gene Expression chromatin SWI/SNF Control Chromatin modification chromatin binding complex methylated histone Binding negative sequence- sequence- sequence- regulation of sequence- specific DNA specific DNA specific DNA transcription specific DNA sequence-specific DNA binding binding binding binding factor activity -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
The New Melanoma: a Novel Model for Disease Progression
DISS. ETH NO. 17606 The new melanoma: A novel model for disease progression A dissertation submitted to SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZÜRICH for the degree of DOCTOR OF SCIENCES presented by Natalie Schlegel Master of Science, Otago University (New Zealand) born on January 20th 1976 citizen of Zürich (ZH) accepted on the recommendation of Professor Sabine Werner, examinor Professor Reinhard Dummer, co-examinor Professor Josef Jiricny, co-examinor 2008 22 Table of Contents Abstract ...................................................................................................................................... 6 Résumé....................................................................................................................................... 8 Abbreviations ........................................................................................................................... 10 1. Introduction ...................................................................................................................... 13 1.1 Definition ................................................................................................................. 14 1.2 Clinical features........................................................................................................ 14 1.3 Pathological features and staging............................................................................. 16 1.3.2 Clark’s level of invasion and Breslow’s thickness........................................... 16 1.3.3 TNM staging ................................................................................................... -
Supplementary Information For
1 Supplementary Information for 2 Molecular map of GNAO1-related disease phenotypes and reactions to therapy 3 Ivana Mihalek, Jeff L. Waugh, Meredith Park, Saima Kayani, Annapurna Poduri, Olaf Bodamer 4 Ivana Mihalek, Olaf Bodamer 5 E-mail: [email protected], [email protected] 6 This PDF file includes: 7 Supplementary text 8 Figs. S1 to S9 9 Tables S1 to S5 10 Legend for Movie S1 11 Legend for Dataset S1 12 SI References 13 Other supplementary materials for this manuscript include the following: 14 Movie S1 15 Dataset S1 Ivana Mihalek, Jeff L. Waugh, Meredith Park, Saima Kayani, Annapurna Poduri, Olaf Bodamer 1 of 18 16 Supporting Information Text 17 Building the target-response profiles 18 To construct the target-response profiles used in Fig. 1 in the main text, we collected the information about the targets of 19 drugs reported as therapy for GNAO1-related symptoms. We collected both the direction of action (up- or down-regulation), 20 and its micromolar activity for each drug-target pair, keeping in mind that drugs typically have multiple targets. The sources 21 were DrugBank (1), BindingDB (2), GuideToPharm (3), PDSP (4), and PubChem (5), as well as manual literature search in a 22 handful of cases. This information is included on Dataset S1. The full collection can be found and downloaded as an SQLite 23 database from the accompanying CodeOcean capsule (codeocean.com/capsule/8747824). 24 Giving the name td to the combined label of target + drug activity direction, for example GABR ↑ for upregulated GABA-A 25 receptor (Human Genome Nomenclature Committee symbol GABR), we assign to it a weight −log (activity) + 6, if the activity is < 106µM w(td) = 10 0 otherwise. -
International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification
0031-6997/03/5504-597–606$7.00 PHARMACOLOGICAL REVIEWS Vol. 55, No. 4 Copyright © 2003 by The American Society for Pharmacology and Experimental Therapeutics 30404/1114803 Pharmacol Rev 55:597–606, 2003 Printed in U.S.A International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on Terms and Symbols in Quantitative Pharmacology RICHARD R. NEUBIG, MICHAEL SPEDDING, TERRY KENAKIN, AND ARTHUR CHRISTOPOULOS Department of Pharmacology, University of Michigan, Ann Arbor, Michigan (R.R.N.); Institute de Recherches Internationales Servier, Neuilly sur Seine, France (M.S.); Systems Research, GlaxoSmithKline Research and Development, Research Triangle Park, North Carolina (T.K.); and Department of Pharmacology, University of Melbourne, Parkville, Australia (A.C.) Abstract ............................................................................... 597 I. Introduction............................................................................ 597 II. Working definition of a receptor .......................................................... 598 III. Use of drugs in definition of receptors or of signaling pathways ............................. 598 A. The expression of amount of drug: concentration and dose ............................... 598 1. Concentration..................................................................... 598 2. Dose. ............................................................................ 598 B. General terms used to describe drug action ........................................... -
Human Ortholog of Drosophila Melted Impedes SMAD2 Release from TGF
Human ortholog of Drosophila Melted impedes SMAD2 PNAS PLUS release from TGF-β receptor I to inhibit TGF-β signaling Premalatha Shathasivama,b,c, Alexandra Kollaraa,c, Maurice J. Ringuetted, Carl Virtanene, Jeffrey L. Wranaa,f, and Theodore J. Browna,b,c,1 aLunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Toronto, ON, Canada M5T 3H7; Departments of bPhysiology, cObstetrics and Gynaecology, dCell and Systems Biology, and fMolecular Genetics, University of Toronto, Toronto, ON, Canada M5S 3G5; and ePrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada M5G 1L7 Edited by Igor B. Dawid, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, and approved May 5, 2015 (received for review March 11, 2015) Drosophila melted encodes a pleckstrin homology (PH) domain- The gene locus encompassing human VEPH1, 3q24-25, lies containing protein that enables normal tissue growth, metabo- within a region frequently amplified in ovarian cancer (6, 7). Tan lism, and photoreceptor differentiation by modulating Forkhead et al. (8) found that this locus was also amplified in 7 of 12 ep- box O (FOXO), target of rapamycin, and Hippo signaling pathways. ithelial ovarian cancer cell lines. A gene copy number analysis of Ventricular zone expressed PH domain-containing 1 (VEPH1) is the 68 primary tumors by Ramakrishna et al. (9) identified frequent mammalian ortholog of melted, and although it exhibits tissue- (>40%) VEPH1 gene amplification that correlated with tran- restricted expression during mouse development and is poten- script levels. We determined the impact of VEPH1 on gene ex- tially amplified in several human cancers, little is known of its pression in an ovarian cancer cell line using a whole-genome function. -
Egfr Activates a Taz-Driven Oncogenic Program in Glioblastoma
EGFR ACTIVATES A TAZ-DRIVEN ONCOGENIC PROGRAM IN GLIOBLASTOMA by Minling Gao A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland March 2020 ©2020 Minling Gao All rights reserved Abstract Hyperactivated EGFR signaling is associated with about 45% of Glioblastoma (GBM), the most aggressive and lethal primary brain tumor in humans. However, the oncogenic transcriptional events driven by EGFR are still incompletely understood. We studied the role of the transcription factor TAZ to better understand master transcriptional regulators in mediating the EGFR signaling pathway in GBM. The transcriptional coactivator with PDZ- binding motif (TAZ) and its paralog gene, the Yes-associated protein (YAP) are two transcriptional co-activators that play important roles in multiple cancer types and are regulated in a context-dependent manner by various upstream signaling pathways, e.g. the Hippo, WNT and GPCR signaling. In GBM cells, TAZ functions as an oncogene that drives mesenchymal transition and radioresistance. This thesis intends to broaden our understanding of EGFR signaling and TAZ regulation in GBM. In patient-derived GBM cell models, EGF induced TAZ and its known gene targets through EGFR and downstream tyrosine kinases (ERK1/2 and STAT3). In GBM cells with EGFRvIII, an EGF-independent and constitutively active mutation, TAZ showed EGF- independent hyperactivation when compared to EGFRvIII-negative cells. These results revealed a novel EGFR-TAZ signaling axis in GBM cells. The second contribution of this thesis is that we performed next-generation sequencing to establish the first genome-wide map of EGF-induced TAZ target genes. -
International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on Terms and Symbols in Quantitative Pharmacology
0031-6997/03/5504-597–606$7.00 PHARMACOLOGICAL REVIEWS Vol. 55, No. 4 Copyright © 2003 by The American Society for Pharmacology and Experimental Therapeutics 30404/1114803 Pharmacol Rev 55:597–606, 2003 Printed in U.S.A International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on Terms and Symbols in Quantitative Pharmacology RICHARD R. NEUBIG, MICHAEL SPEDDING, TERRY KENAKIN, AND ARTHUR CHRISTOPOULOS Department of Pharmacology, University of Michigan, Ann Arbor, Michigan (R.R.N.); Institute de Recherches Internationales Servier, Neuilly sur Seine, France (M.S.); Systems Research, GlaxoSmithKline Research and Development, Research Triangle Park, North Carolina (T.K.); and Department of Pharmacology, University of Melbourne, Parkville, Australia (A.C.) Abstract ............................................................................... 597 I. Introduction............................................................................ 597 II. Working definition of a receptor .......................................................... 598 III. Use of drugs in definition of receptors or of signaling pathways ............................. 598 A. The expression of amount of drug: concentration and dose ............................... 598 Downloaded from 1. Concentration..................................................................... 598 2. Dose. ............................................................................ 598 B. General terms used to describe drug action ........................................... -
Systematic Elucidation of Neuron-Astrocyte Interaction in Models of Amyotrophic Lateral Sclerosis Using Multi-Modal Integrated Bioinformatics Workflow
ARTICLE https://doi.org/10.1038/s41467-020-19177-y OPEN Systematic elucidation of neuron-astrocyte interaction in models of amyotrophic lateral sclerosis using multi-modal integrated bioinformatics workflow Vartika Mishra et al.# 1234567890():,; Cell-to-cell communications are critical determinants of pathophysiological phenotypes, but methodologies for their systematic elucidation are lacking. Herein, we propose an approach for the Systematic Elucidation and Assessment of Regulatory Cell-to-cell Interaction Net- works (SEARCHIN) to identify ligand-mediated interactions between distinct cellular com- partments. To test this approach, we selected a model of amyotrophic lateral sclerosis (ALS), in which astrocytes expressing mutant superoxide dismutase-1 (mutSOD1) kill wild-type motor neurons (MNs) by an unknown mechanism. Our integrative analysis that combines proteomics and regulatory network analysis infers the interaction between astrocyte-released amyloid precursor protein (APP) and death receptor-6 (DR6) on MNs as the top predicted ligand-receptor pair. The inferred deleterious role of APP and DR6 is confirmed in vitro in models of ALS. Moreover, the DR6 knockdown in MNs of transgenic mutSOD1 mice attenuates the ALS-like phenotype. Our results support the usefulness of integrative, systems biology approach to gain insights into complex neurobiological disease processes as in ALS and posit that the proposed methodology is not restricted to this biological context and could be used in a variety of other non-cell-autonomous communication -
Characterizing Genomic Duplication in Autism Spectrum Disorder by Edward James Higginbotham a Thesis Submitted in Conformity
Characterizing Genomic Duplication in Autism Spectrum Disorder by Edward James Higginbotham A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Molecular Genetics University of Toronto © Copyright by Edward James Higginbotham 2020 i Abstract Characterizing Genomic Duplication in Autism Spectrum Disorder Edward James Higginbotham Master of Science Graduate Department of Molecular Genetics University of Toronto 2020 Duplication, the gain of additional copies of genomic material relative to its ancestral diploid state is yet to achieve full appreciation for its role in human traits and disease. Challenges include accurately genotyping, annotating, and characterizing the properties of duplications, and resolving duplication mechanisms. Whole genome sequencing, in principle, should enable accurate detection of duplications in a single experiment. This thesis makes use of the technology to catalogue disease relevant duplications in the genomes of 2,739 individuals with Autism Spectrum Disorder (ASD) who enrolled in the Autism Speaks MSSNG Project. Fine-mapping the breakpoint junctions of 259 ASD-relevant duplications identified 34 (13.1%) variants with complex genomic structures as well as tandem (193/259, 74.5%) and NAHR- mediated (6/259, 2.3%) duplications. As whole genome sequencing-based studies expand in scale and reach, a continued focus on generating high-quality, standardized duplication data will be prerequisite to addressing their associated biological mechanisms. ii Acknowledgements I thank Dr. Stephen Scherer for his leadership par excellence, his generosity, and for giving me a chance. I am grateful for his investment and the opportunities afforded me, from which I have learned and benefited. I would next thank Drs. -
Matters of Receptor Conformation
Inverse, protean, and ligand-selective agonism: matters of receptor conformation TERRY KENAKIN Department of Receptor Biochemistry, Glaxo SmithKline Research and Development, Research Triangle Park, North Carolina 27709, USA ABSTRACT Concepts regarding the mechanisms by allosteric interaction resulted in an alteration of the which drugs activate receptors to produce physiological affinity of the receptor for the radiolabel. With the response have progressed beyond considering the re- technological advances enabling high-throughput func- ceptor as a simple on-off switch. Current evidence tional receptor screens should come an increase in the suggests that the idea that agonists produce only vary- types of GPCR ligands in the new millennium. This will ing degrees of receptor activation is obsolete and must result in an increase in the number of allosteric ligands be reconciled with data to show that agonist efficacy has (modulators, agonists, enhancers) that modify receptor texture as well as magnitude. Thus, agonists can block function without necessarily modifying steric access of system constitutive response (inverse agonists), behave the endogenous ligands to the receptor. Therefore, the as positive and inverse agonists on the same receptor changing mode of high-throughput screening can be (protean agonists), and differ in the stimulus pattern predicted to lead to an increase in the texture of drug they produce in physiological systems (ligand-selective types for GPCRs. Another reason for the increasing agonists). The molecular mechanism for this seemingly number of drug targets is increased knowledge of diverse array of activities is the same, namely, the GPCR behavior in cellular systems. This has led to the selective microaffinity of ligands for different confor- discovery of inverse agonism.