A Genome-Wide Positioning Systems Network Algorithm for in Silico Drug Repurposing
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Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
The University of Chicago Genetic Services Laboratories
The University of Chicago Genetic Services Laboratories 5841 S. Maryland Ave., Rm. G701, MC 0077, Chicago, Illinois 60637 Toll Free: (888) UC GENES (888) 824 3637 Local: (773) 834 0555 FAX: (773) 702 9130 [email protected] dnatesting.uchicago.edu. CLIA #: 14D0917593 CAP #: 18827-49 Next Generation Sequencing Panel for Early Infantile Epileptic Encephalopathy Clinical Features and Molecular Genetics: Early infantile epileptic encephalopathy (EIEE), also known as Ohtahara syndrome, is a severe form of epilepsy characterized by frequent tonic spasms with onset in the first months of life. EEG reveals suppression-burst patterns, characterized by high- voltage bursts alternating with almost flat suppression phases. Seizures are medically intractable with evolution to West syndrome at 3-6 months of age and then Lennox-Gastaut syndrome at 1-3 years of age. EIEE represents approximately 1% of all epilepsies occuring in children less than 15 years of age (1). Patients have severe developmental delay and poor prognosis. The diagnostic workup of EIEEs remains challenging because of frequent difficulties in defining etiologies. Acquired structural abnormalities like hypoxic-ischemic insults and isolated cortical malformations, which represent the most common causes of epileptic encelphalopathy in infancy should be excluded first (2). Our EIEE Panel includes sequence analysis of all 15 genes listed below, and deletion/duplication analysis of the 12 genes listed in bold below. Early Infantile Epileptic Encephalopathy Panel ARHGEF9 KCNQ2 PNKP SLC2A1 ARX MAGI2 SCN1A SPTAN1 CDKL5 PCDH19 SCN2A STXBP1 GRIN2A PLCB1 SLC25A22 Gene / Condition Clinical Features and Molecular Pathology ARHGEF9 The ARHGEF9 gene encodes the protein collybistin, which is a brain-specific protein involved in EIEE8 inhibitory synaptogenesis (3). -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Identification of the Binding Partners for Hspb2 and Cryab Reveals
Brigham Young University BYU ScholarsArchive Theses and Dissertations 2013-12-12 Identification of the Binding arP tners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non- Redundant Roles for Small Heat Shock Proteins Kelsey Murphey Langston Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Microbiology Commons BYU ScholarsArchive Citation Langston, Kelsey Murphey, "Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins" (2013). Theses and Dissertations. 3822. https://scholarsarchive.byu.edu/etd/3822 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science Julianne H. Grose, Chair William R. McCleary Brian Poole Department of Microbiology and Molecular Biology Brigham Young University December 2013 Copyright © 2013 Kelsey Langston All Rights Reserved ABSTRACT Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactors and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston Department of Microbiology and Molecular Biology, BYU Master of Science Small Heat Shock Proteins (sHSP) are molecular chaperones that play protective roles in cell survival and have been shown to possess chaperone activity. -
Acebutolol Hydrochloride Eq 200 Mg Base, Capsule, Oral, 100 0.4612 B Eq 400 Mg Base, Capsule, Oral, 100 0.6713 B
TRANSMITTAL NO. 37 - FEDERAL UPPER LIMIT DRUG LIST NOVEMBER 20, 2001 The following list of multiple source drugs meets the criteria set forth in 42 CFR 447.332 and Section 1927(e) of the Social Security Act, as amended by OBRA 1993. Payment for multiple source drugs identified and listed below must not exceed, in the aggregate, payment levels determined by applying to each drug entity a reasonable dispensing fee (established by the State and specified in the State plan), plus an amount based on the limit per unit which CMS has determined to be equal to 150 percent applied to the lowest price listed (in package sizes of 100 units, unless otherwise noted) in any of the published compendia of cost information of drugs. The listing is based on data current as of April 2001 from First Data Bank (Blue Book), Medi- Span, and the Red Book. This list does not reference the commonly known brand names. However, the brand names are included in the electronic FUL listing provided to the state agencies. The FUL price list and electronic listing are available at http://www.cms.hhs.gov/Reimbursement/05_FederalUpperLimits.asp. In accordance with current policy, Federal financial participation will not be provided for any drug on the FUL listing for which the FDA has issued a notice of an opportunity for a hearing as a result of the Drug Efficacy Study and Implementation (DESI) program, and which has been found to be a less than effective or is identical, related or similar (IRS) to the DESI drug. The DESI drug is identified by the Food and Drug Administration or reported by the drug manufacturer for purposes of the Medicaid drug rebate program. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
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. -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
Selective Labeling of Serotonin Receptors Byd-[3H]Lysergic Acid
Proc. Nati. Acad. Sci. USA Vol. 75, No. 12, pp. 5783-5787, December 1978 Biochemistry Selective labeling of serotonin receptors by d-[3H]lysergic acid diethylamide in calf caudate (ergots/hallucinogens/tryptamines/norepinephrine/dopamine) PATRICIA M. WHITAKER AND PHILIP SEEMAN* Department of Pharmacology, University of Toronto, Toronto, Canada M5S 1A8 Communicated by Philip Siekevltz, August 18,1978 ABSTRACT Since it was known that d-lysergic acid di- The objective in this present study was to improve the se- ethylamide (LSD) affected catecholaminergic as well as sero- lectivity of [3H]LSD for serotonin receptors, concomitantly toninergic neurons, the objective in this study was to enhance using other drugs to block a-adrenergic and dopamine receptors the selectivity of [3HJISD binding to serotonin receptors in vitro by using crude homogenates of calf caudate. In the presence of (cf. refs. 36-38). We then compared the potencies of various a combination of 50 nM each of phentolamine (adde to pre- drugs on this selective [3H]LSD binding and compared these clude the binding of [3HJLSD to a-adrenoceptors), apmo ie, data to those for the high-affinity binding of [3H]serotonin and spiperone (added to preclude the binding of [3H[LSD to (39). dopamine receptors), it was found by Scatchard analysis that the total number of 3H sites went down to 300 fmol/mg, compared to 1100 fmol/mg in the absence of the catechol- METHODS amine-blocking drugs. The IC50 values (concentrations to inhibit Preparation of Membranes. Calf brains were obtained fresh binding by 50%) for various drugs were tested on the binding of [3HLSD in the presence of 50 nM each of apomorphine (A), from the Canada Packers Hunisett plant (Toronto). -
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Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes. -
Topical and Systemic Antifungal Therapy for Chronic Rhinosinusitis (Protocol)
CORE Metadata, citation and similar papers at core.ac.uk Provided by University of East Anglia digital repository Cochrane Database of Systematic Reviews Topical and systemic antifungal therapy for chronic rhinosinusitis (Protocol) Head K, Sacks PL, Chong LY, Hopkins C, Philpott C Head K, Sacks PL, Chong LY, Hopkins C, Philpott C. Topical and systemic antifungal therapy for chronic rhinosinusitis. Cochrane Database of Systematic Reviews 2016, Issue 11. Art. No.: CD012453. DOI: 10.1002/14651858.CD012453. www.cochranelibrary.com Topical and systemic antifungal therapy for chronic rhinosinusitis (Protocol) Copyright © 2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. TABLE OF CONTENTS HEADER....................................... 1 ABSTRACT ...................................... 1 BACKGROUND .................................... 1 OBJECTIVES ..................................... 3 METHODS ...................................... 3 ACKNOWLEDGEMENTS . 8 REFERENCES ..................................... 9 APPENDICES ..................................... 10 CONTRIBUTIONSOFAUTHORS . 25 DECLARATIONSOFINTEREST . 26 SOURCESOFSUPPORT . 26 NOTES........................................ 26 Topical and systemic antifungal therapy for chronic rhinosinusitis (Protocol) i Copyright © 2016 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. [Intervention Protocol] Topical and systemic antifungal therapy for chronic rhinosinusitis Karen Head1, Peta-Lee Sacks2, Lee Yee Chong1, Claire Hopkins3, Carl Philpott4 1UK Cochrane Centre, -
Therapeutic Class Overview Beta-Adrenergic Blocking Agents
Therapeutic Class Overview Beta-adrenergic Blocking Agents INTRODUCTION Approximately 92.1 million American adults have at least 1 type of cardiovascular disease according to the American Heart Association (AHA) Heart Disease and Stroke Statistics 2018 update. From 2003 to 2015, mortality associated with cardiovascular disease declined 25.5% (Benjamin et al 2018). Beta-adrenergic blocking agents (beta-blockers) are a group of drugs that block the sympathomimetic effects of catecholamines on beta receptors. This results in negative inotropic and chronotropic effects and relaxation of smooth muscle. Beta-blockers have varied pharmacologic properties. ○ Cardioselective beta-blockers preferentially interact with beta1-receptors, which are predominantly found in the heart. Non-cardioselective beta-blockers also interact with beta2-receptors found on smooth muscle in the lungs, blood vessels, and other tissues. The cardioselectivity of beta-blockers is dose dependent; therefore, beta2 blockade can occur at higher doses with certain cardioselective agents. ○ Some beta-blockers (acebutolol and pindolol) have intrinsic sympathomimetic activity (ISA), which may result in a lower incidence of bradycardia and bronchoconstriction (Facts and Comparisons 2018). In addition, some beta- blockers (nebivolol and propranolol) have higher lipophilicity, which may increase the risk for central nervous system- related adverse events (Facts and Comparisons 2018). ○ Carvedilol and labetalol also block alpha-adrenergic receptors and may reduce peripheral resistance more than other beta-blockers (Clinical Pharmacology 2018). Specific indications for the beta-blockers vary by product. Most beta-blockers (all except sotalol) are approved to treat hypertension (HTN). The 2017 American College of Cardiology (ACC)/AHA clinical practice guideline defines HTN as blood pressure (BP) ≥ 130/80 mm Hg (Whelton et al 2017).