The Protocadherins, PCDHB1 and PCDH7, Are Regulated by Mecp2 in Neuronal Cells and Brain Tissues
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Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
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. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Learning from Cadherin Structures and Sequences: Affinity Determinants and Protein Architecture
Learning from cadherin structures and sequences: affinity determinants and protein architecture Klára Fels ıvályi 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 Klara Felsovalyi All rights reserved ABSTRACT Learning from cadherin structures and sequences: affinity determinants and protein architecture Klara Felsovalyi Cadherins are a family of cell-surface proteins mediating adhesion that are important in development and maintenance of tissues. The family is defined by the repeating cadherin domain (EC) in their extracellular region, but they are diverse in terms of protein size, architecture and cellular function. The best-understood subfamily is the type I classical cadherins, which are found in vertebrates and have five EC domains. Among the five different type I classical cadherins, the binding interactions are highly specific in their homo- and heterophilic binding affinities, though their sequences are very similar. As previously shown, E- and N-cadherins, two prototypic members of the subfamily, differ in their homophilic K D by about an order of magnitude, while their heterophilic affinity is intermediate. To examine the source of the binding affinity differences among type I cadherins, we used crystal structures, analytical ultracentrifugation (AUC), surface plasmon resonance (SPR), and electron paramagnetic resonance (EPR) studies. Phylogenetic analysis and binding affinity behavior show that the type I cadherins can be further divided into two subgroups, with E- and N-cadherin representing each. In addition to the affinity differences in their wild-type binding through the strand-swapped interface, a second interface also shows an affinity difference between E- and N-cadherin. -
Toxicogenomics Applications of New Functional Genomics Technologies in Toxicology
\-\w j Toxicogenomics Applications of new functional genomics technologies in toxicology Wilbert H.M. Heijne Proefschrift ter verkrijging vand egraa dva n doctor opgeza gva nd e rector magnificus vanWageninge n Universiteit, Prof.dr.ir. L. Speelman, in netopenbaa r te verdedigen op maandag6 decembe r200 4 des namiddagst e half twee ind eAul a - Table of contents Abstract Chapter I. page 1 General introduction [1] Chapter II page 21 Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach[2] Chapter III page 48 Bromobenzene-induced hepatotoxicity atth etranscriptom e level PI Chapter IV page 67 Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis[4] Chapter V page 88 Liver gene expression profiles in relation to subacute toxicity in rats exposed to benzene[5] Chapter VI page 115 Toxicogenomics analysis of liver gene expression in relation to subacute toxicity in rats exposed totrichloroethylen e [6] Chapter VII page 135 Toxicogenomics analysis ofjoin t effects of benzene and trichloroethylene mixtures in rats m Chapter VII page 159 Discussion and conclusions References page 171 Appendices page 187 Samenvatting page 199 Dankwoord About the author Glossary Abbreviations List of genes Chapter I General introduction Parts of this introduction were publishedin : Molecular Biology in Medicinal Chemistry, Heijne etal., 2003 m NATO Advanced Research Workshop proceedings, Heijne eral., 2003 81 Chapter I 1. General introduction 1.1 Background /.1.1 Toxicologicalrisk -
Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors
ARTICLE DOI: 10.1038/s41467-018-06366-z OPEN Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors Sudheer Kumar Gara1, Justin Lack2, Lisa Zhang1, Emerson Harris1, Margaret Cam2 & Electron Kebebew1,3 Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is 1234567890():,; unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37–57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs. 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2 Center for Cancer Research, Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3 Department of Surgery and Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Correspondence and requests for materials should be addressed to E.K. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4172 | DOI: 10.1038/s41467-018-06366-z | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06366-z drenocortical carcinoma (ACC) is a rare malignancy with types including primary ACC from the TCGA to understand our A0.7–2 cases per million per year1,2. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
1 Self-Maintaining Gut Macrophages Are Essential for Intestinal
Self-maintaining Gut Macrophages Are Essential for Intestinal Homeostasis De Schepper Sebastiaan1*, Verheijden Simon1*†, Aguilera-Lizarraga Javi1, Viola Maria Francesca1, Boesmans Werend2, Stakenborg Nathalie1, Voytyuk Iryna3, Smidt Inga3, Boeckx Bram4,5, Dierckx de Casterlé Isabelle6, Baekelandt Veerle7, Gonzalez Dominguez Erika1, Mack Matthias8, Depoortere Inge9, De Strooper Bart3, Sprangers Ben4, Himmelreich Uwe10, Soenen Stefaan10, Guilliams Martin11, Vanden Berghe Pieter2, Jones Elizabeth12, Lambrechts Diether5,6, Boeckxstaens Guy1 1Laboratory for Intestinal Neuroimmune Interactions, Translational Research Center for Gastrointestinal Disorders, Department of Chronic Diseases, Metabolism and Ageing, University of Leuven, Leuven, Belgium. 2Laboratory for Enteric Neuroscience, Translational Research Center for Gastrointestinal Disorders, Department of Chronic Diseases, Metabolism and Ageing, University of Leuven, Leuven, Belgium. 3VIB – KU Leuven Center for Brain and Disease Research, Leuven, Belgium. 4VIB Center for Cancer Biology, Leuven, Belgium. 5Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium. 6Laboratory of Experimental Transplantation, Department of Microbiology and Immunology, University of Leuven, Leuven, Belgium. 7Research Group for Neurobiology and Gene Therapy, Department of Neurosciences, University of Leuven, Leuven, Belgium. 8Department of Internal Medicine – Nephrology, University Hospital Regensburg, Regensburg, Germany. 9Gut Peptide Research Lab, Translational -
Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing -
A Discovery Resource of Rare Copy Number Variations in Individuals with Autism Spectrum Disorder
INVESTIGATION A Discovery Resource of Rare Copy Number Variations in Individuals with Autism Spectrum Disorder Aparna Prasad,* Daniele Merico,* Bhooma Thiruvahindrapuram,* John Wei,* Anath C. Lionel,*,† Daisuke Sato,* Jessica Rickaby,* Chao Lu,* Peter Szatmari,‡ Wendy Roberts,§ Bridget A. Fernandez,** Christian R. Marshall,*,†† Eli Hatchwell,‡‡ Peggy S. Eis,‡‡ and Stephen W. Scherer*,†,††,1 *The Centre for Applied Genomics, Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto M5G 1L7, Canada, †Department of Molecular Genetics, University of Toronto, Toronto M5G 1L7, Canada, ‡Offord Centre for Child Studies, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton L8P 3B6, § Canada, Autism Research Unit, The Hospital for Sick Children, Toronto M5G 1X8, Canada, **Disciplines of Genetics and Medicine, Memorial University of Newfoundland, St. John’s, Newfoundland A1B 3V6, Canada, ††McLaughlin Centre, University of Toronto, Toronto M5G 1L7, Canada, and ‡‡Population Diagnostics, Inc., Melville, New York 11747 ABSTRACT The identification of rare inherited and de novo copy number variations (CNVs) in human KEYWORDS subjects has proven a productive approach to highlight risk genes for autism spectrum disorder (ASD). A rare variants variety of microarrays are available to detect CNVs, including single-nucleotide polymorphism (SNP) arrays gene copy and comparative genomic hybridization (CGH) arrays. Here, we examine a cohort of 696 unrelated ASD number cases using a high-resolution one-million feature CGH microarray, the majority of which were previously chromosomal genotyped with SNP arrays. Our objective was to discover new CNVs in ASD cases that were not detected abnormalities by SNP microarray analysis and to delineate novel ASD risk loci via combined analysis of CGH and SNP array cytogenetics data sets on the ASD cohort and CGH data on an additional 1000 control samples. -
Research Article
z Available online at http://www.journalcra.com INTERNATIONAL JOURNAL OF CURRENT RESEARCH International Journal of Current Research Vol. 10, Issue, 05, pp.69389-69394, May, 2018 ISSN: 0975-833X RESEARCH ARTICLE PATHWAY MAPPING OF PROTEINS INVOLVED IN MULTIPLE SCLEROSIS AND IDENTIFICATION OF NOVEL PROTEINS *Ruchi Yadav and Samreen Rizvi AMITY Institute of Biotechnology, AMITY University, Uttar Pradesh Lucknow Campus, 226028, UP, India ARTICLE INFO ABSTRACT Article History: WASH in the urban areas Multiple sclerosis is an unpredictable potentially disabling disease that Received 14th February, 2018 causes disruption of the myelin that insulates and protects nerve cells of spinal cord and brain and Received in revised form permits electrical impulses to be conducted along the nerve fiber with speed and accuracy. So this 02nd March, 2018 disease disrupts central nervous system by damaging or demyelinating the insulated covers of nerve Accepted 09th April, 2018 cells. In this paper we have made network of interacting proteins of more than 300 interacting Published online 30th May, 2018 proteins identified from protein interaction database .Initially 14 proteins were identified from Uni Prot database that were involved in multiple sclerosis disease . Gene Cards database was used to Key words: study pathways of proteins involved in network and new network was made that classifies interacting proteins on the basis of pathways involves. We found Three proteins, APBB2, APBB3 and MMP15 Multiple Sclerosis, Pathway Mapping, having no information about pathway and disease in which they are involved. New Network was Protein Interaction, String Database, Gene designed to predicted pathway of these novel proteins, APBB2, APBB3 and MMP15, network Mania Database. -
Termination of RNA Polymerase II Transcription by the 5’-3’ Exonuclease Xrn2
TERMINATION OF RNA POLYMERASE II TRANSCRIPTION BY THE 5’-3’ EXONUCLEASE XRN2 by MICHAEL ANDRES CORTAZAR OSORIO B.S., Universidad del Valle – Colombia, 2011 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Molecular Biology Program 2018 This thesis for the Doctor of Philosophy degree by Michael Andrés Cortázar Osorio has been approved for the Molecular Biology Program by Mair Churchill, Chair Richard Davis Jay Hesselberth Thomas Blumenthal James Goodrich David Bentley, Advisor Date: Aug 17, 2018 ii Cortázar Osorio, Michael Andrés (Ph.D., Molecular Biology) Termination of RNA polymerase II transcription by the 5’-3’ exonuclease Xrn2 Thesis directed by Professor David L. Bentley ABSTRACT Termination of transcription occurs when RNA polymerase (pol) II dissociates from the DNA template and releases a newly-made mRNA molecule. Interestingly, an active debate fueled by conflicting reports over the last three decades is still open on which of the two main models of termination of RNA polymerase II transcription does in fact operate at 3’ ends of genes. The torpedo model indicates that the 5’-3’ exonuclease Xrn2 targets the nascent transcript for degradation after cleavage at the polyA site and chases pol II for termination. In contrast, the allosteric model asserts that transcription through the polyA signal induces a conformational change of the elongation complex and converts it into a termination-competent complex. In this thesis, I propose a unified allosteric-torpedo mechanism. Consistent with a polyA site-dependent conformational change of the elongation complex, I found that pol II transitions at the polyA site into a mode of slow transcription elongation that is accompanied by loss of Spt5 phosphorylation in the elongation complex.