Towards a Comprehensive Description of the Human Retinal Transcriptome

Total Page:16

File Type:pdf, Size:1020Kb

Towards a Comprehensive Description of the Human Retinal Transcriptome Towards a Comprehensive Description of the Human Retinal Transcriptome: Identification and Characterization of Differentially Expressed Genes Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Heidi Schulz aus Argentinien Würzburg 2003 Eingereicht am 10. September 2003 Bei der Fakultät für Biologie Mitglieder der Promotionskommission Vorsitzender: Prof. Dr. R. Hedrich Gutachter: Prof. Dr. Bernhard H.F. Weber Gutachter: Prof. Dr. Ricardo Benavente Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am: Erklärung gemäß §4, Absatz 3 der Promotionsordnung für die Fakultät für Biologie der Universität Würzburg: Hiermit erkläre ich, dass ich die vorliegende Dissertation selbständig durchgeführt und verfasst habe. Andere Quellen als die angegebenen Hilfsmittel und Quellen wurden nicht verwendet. Die Dissertation wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Es wurde zuvor kein anderer akademischer Grad erworben. Die vorliegende Arbeit wurde am Institut für Humangenetik der Universität Würzburg unter der Leitung von Prof. Bernhard H.F. Weber angefertigt. Würzburg, den 10. September 2003 ACKNOWLEDGMENTS I would like to thank Prof. Bernhard Weber for giving me the possibility to do my Ph.D. research in his group and for coaching me in the fine art of scientific investigation and writing. My gratitude also extends to Prof. Ricardo Benavente who as a member of the Faculty of Biology of the University of Würzburg accepted to be supervisor of this thesis. I would like to acknowledge Dr. Heidi Stöhr for introducing me to the world of gene identification and characterization. I would also like to express my gratitude to past and present lab members of the AG Weber who helped me and contributed to a very pleasant working atmosphere. In particular I thank Jelena Stojic for the great working relationship, Vladimir Milenkovic for his patient assistance in all computer-related problems, Susanne Fröhlich and Claudia Berger for helping with the RT-PCR expression analyses, Franziska Krämer and Christine Wiedemann for the proofreading assistance, and Andrea Rivera for always being there. I am deeply indebted to my family. You really deserve far more credit than I can ever give you! Just to mention a few things, I thank you for your generous love, support, advice, and for teaching me not with words but with your actions and life. Your integrity and warmth provided a wonderful environment in which to grow. My most heartfelt thanks go to my husband Lucio. Without his strength, patience, and comprehension, it would not have been possible for me to finish this dissertation. Through the hardships of graduate studies, my resolve remained strong because of his love and understanding. My final recognition and thanks go to God for His everlasting love, guidance, the health He gave me these years, and for being my strength. Table of Contents Table of Contents I Summary ................................................................................................................................. 1 II Zusammenfassung ........................................................................................................................... 4 III Introduction 1. The human retina ........................................................................................................................................ 7 2. Hereditary retinal degenerations ................................................................................................................. 9 2.1. The age-related macular degeneration (AMD)................................................................................... 10 3. Molecular genetics of human retinal disease ............................................................................................ 10 4. Gene identification approaches for monogenic disorders ......................................................................... 11 5. Gene identification approaches for complex disorders ............................................................................. 13 6. Contributions of the genomic era to gene identification efforts ................................................................. 15 7. Goal of the thesis ...................................................................................................................................... 18 IV Material and Methods 1. Bioinformatic tools ..................................................................................................................................... 19 1.1. Databases.......................................................................................................................................... 19 1.1.1. Sequence databases............................................................................................................... 19 1.1.1.1. GenBank ......................................................................................................................... 19 1.1.1.2. Databases of expressed sequence tags (EST)............................................................... 19 1.1.1.3. UniGene .......................................................................................................................... 19 1.1.1.4. TIGR................................................................................................................................ 20 1.1.2. Gene information databases ................................................................................................... 20 1.1.3. Databases of genes involved in disease ................................................................................. 20 1.1.4. Other databases...................................................................................................................... 21 1.1.4.1. PubMed........................................................................................................................... 21 1.1.4.2. HUGO Gene Nomenclature (HGCN) Committee............................................................ 21 1.1.4.3. HuGEIndex Gene Specific Expression database............................................................ 21 1.2. Sequence analysis tools .................................................................................................................... 21 1.2.1. Splice site discrimination score ............................................................................................... 22 2. RNA-related methods................................................................................................................................ 23 2.1. Sources.............................................................................................................................................. 23 2.1.1. Tissue ...................................................................................................................................... 23 2.1.2. RNA......................................................................................................................................... 23 2.2. RNA isolation ..................................................................................................................................... 23 2.3. Elimination of contaminating genomic DNA....................................................................................... 23 2.4. Northern blot analysis ........................................................................................................................ 24 2.4.1. Filter preparation ..................................................................................................................... 24 2.4.2. Probe labeling.......................................................................................................................... 24 2.4.3. Membrane hybridization and washing ..................................................................................... 24 2.5. First-strand cDNA synthesis............................................................................................................... 25 3. DNA-related methods................................................................................................................................ 25 3.1. Isolation of plasmid DNA.................................................................................................................... 25 3.2. Polymerase chain reaction (PCR)...................................................................................................... 25 3.3. Relative quantitative real-time PCR (qRT-PCR) ................................................................................ 26 3.3.1. Primer design .......................................................................................................................... 26 3.3.2. Optimization of qRT-PCR reactions ........................................................................................ 26 3.3.3. Determination of amplification efficiency ................................................................................. 26 3.3.4. qRT-PCR Protocol................................................................................................................... 27 3.3.5. Data analysis........................................................................................................................... 27 3.4. Agarose gel electrophoresis..............................................................................................................
Recommended publications
  • Inhibition of Breast and Brain Cancer Cell Growth by Bccipα, An
    Oncogene (2001) 20, 336 ± 345 ã 2001 Nature Publishing Group All rights reserved 0950 ± 9232/01 $15.00 www.nature.com/onc Inhibition of breast and brain cancer cell growth by BCCIPa,an evolutionarily conserved nuclear protein that interacts with BRCA2 Jingmei Liu1, Yuan Yuan1,2, Juan Huan2 and Zhiyuan Shen*,1 1Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center; 915 Camino de Salud, NE. Albuquerque, New Mexico, NM 87131, USA; 2Graduate Program of Molecular Genetics, College of Medicine, University of Illinois at Chicago, 900 S. Ashland Ave. Chicago, Illinois, IL 60607, USA BRCA2 is a tumor suppressor gene involved in mammary mouse BRCA2. It is expected that important functions tumorigenesis. Although important functions have been of BRCA2 reside in these conserved domains. Based on assigned to a few conserved domains of BRCA2, little is the functional analysis of the conserved BRCA2 known about the longest internal conserved domain domains, several models have been proposed for the encoded by exons 14 ± 24. We identi®ed a novel protein, role of BRCA2 in tumor suppression. designated BCCIPa, that interacts with part of the An N-terminus conserved domain in exon 3 (amino internal conserved region of human BRCA2. Human acids 48 ± 105) has been implicated in transcriptional BCCIP represents a family of proteins that are regulation of gene expression (Milner et al., 1997; evolutionarily conserved, and contain three distinct Nordling et al., 1998). Deletion of this region has been domains: an N-terminus acidic domain (NAD) of 30 ± identi®ed in breast cancers (Nordling et al., 1998).
    [Show full text]
  • 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.
    [Show full text]
  • Nuclear and Mitochondrial Genome Defects in Autisms
    UC Irvine UC Irvine Previously Published Works Title Nuclear and mitochondrial genome defects in autisms. Permalink https://escholarship.org/uc/item/8vq3278q Journal Annals of the New York Academy of Sciences, 1151(1) ISSN 0077-8923 Authors Smith, Moyra Spence, M Anne Flodman, Pamela Publication Date 2009 DOI 10.1111/j.1749-6632.2008.03571.x License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California THE YEAR IN HUMAN AND MEDICAL GENETICS 2009 Nuclear and Mitochondrial Genome Defects in Autisms Moyra Smith, M. Anne Spence, and Pamela Flodman Department of Pediatrics, University of California, Irvine, California In this review we will evaluate evidence that altered gene dosage and structure im- pacts neurodevelopment and neural connectivity through deleterious effects on synap- tic structure and function, and evidence that the latter are key contributors to the risk for autism. We will review information on alterations of structure of mitochondrial DNA and abnormal mitochondrial function in autism and indications that interactions of the nuclear and mitochondrial genomes may play a role in autism pathogenesis. In a final section we will present data derived using Affymetrixtm SNP 6.0 microar- ray analysis of DNA of a number of subjects and parents recruited to our autism spectrum disorders project. We include data on two sets of monozygotic twins. Col- lectively these data provide additional evidence of nuclear and mitochondrial genome imbalance in autism and evidence of specific candidate genes in autism. We present data on dosage changes in genes that map on the X chromosomes and the Y chro- mosome.
    [Show full text]
  • Dynamics of Gene Silencing During X Inactivation Using Allele-Specific RNA-Seq Hendrik Marks1*, Hindrik H
    Marks et al. Genome Biology (2015) 16:149 DOI 10.1186/s13059-015-0698-x RESEARCH Open Access Dynamics of gene silencing during X inactivation using allele-specific RNA-seq Hendrik Marks1*, Hindrik H. D. Kerstens1, Tahsin Stefan Barakat3, Erik Splinter4, René A. M. Dirks1, Guido van Mierlo1, Onkar Joshi1, Shuang-Yin Wang1, Tomas Babak5, Cornelis A. Albers2, Tüzer Kalkan6, Austin Smith6, Alice Jouneau7, Wouter de Laat4, Joost Gribnau3 and Hendrik G. Stunnenberg1* Abstract Background: During early embryonic development, one of the two X chromosomes in mammalian female cells is inactivated to compensate for a potential imbalance in transcript levels with male cells, which contain a single X chromosome. Here, we use mouse female embryonic stem cells (ESCs) with non-random X chromosome inactivation (XCI) and polymorphic X chromosomes to study the dynamics of gene silencing over the inactive X chromosome by high-resolution allele-specific RNA-seq. Results: Induction of XCI by differentiation of female ESCs shows that genes proximal to the X-inactivation center are silenced earlier than distal genes, while lowly expressed genes show faster XCI dynamics than highly expressed genes. The active X chromosome shows a minor but significant increase in gene activity during differentiation, resulting in complete dosage compensation in differentiated cell types. Genes escaping XCI show little or no silencing during early propagation of XCI. Allele-specific RNA-seq of neural progenitor cells generated from the female ESCs identifies three regions distal to the X-inactivation center that escape XCI. These regions, which stably escape during propagation and maintenance of XCI, coincide with topologically associating domains (TADs) as present in the female ESCs.
    [Show full text]
  • Directed Proximity-Dependent Biotin Labelling in Zebrafish
    TOOLS AND RESOURCES In vivo proteomic mapping through GFP- directed proximity-dependent biotin labelling in zebrafish Zherui Xiong1, Harriet P Lo1, Kerrie-Ann McMahon1, Nick Martel1, Alun Jones1, Michelle M Hill2, Robert G Parton1,3*, Thomas E Hall1* 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia; 2QIMR Berghofer Medical Research Institute, Herston, Australia; 3Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Australia Abstract Protein interaction networks are crucial for complex cellular processes. However, the elucidation of protein interactions occurring within highly specialised cells and tissues is challenging. Here, we describe the development, and application, of a new method for proximity- dependent biotin labelling in whole zebrafish. Using a conditionally stabilised GFP-binding nanobody to target a biotin ligase to GFP-labelled proteins of interest, we show tissue-specific proteomic profiling using existing GFP-tagged transgenic zebrafish lines. We demonstrate the applicability of this approach, termed BLITZ (Biotin Labelling In Tagged Zebrafish), in diverse cell types such as neurons and vascular endothelial cells. We applied this methodology to identify interactors of caveolar coat protein, cavins, in skeletal muscle. Using this system, we defined specific interaction networks within in vivo muscle cells for the closely related but functionally distinct Cavin4 and Cavin1 proteins. *For correspondence: Introduction [email protected] (RGP); [email protected] (TEH) The understanding of the biological functions of a protein requires detailed knowledge of the mole- cules with which it interacts. However, robust elucidation of interacting proteins, including not only Competing interests: The strong direct protein-protein interactions, but also weak, transient or indirect interactions is challeng- authors declare that no ing.
    [Show full text]
  • Open Dogan Phdthesis Final.Pdf
    The Pennsylvania State University The Graduate School Eberly College of Science ELUCIDATING BIOLOGICAL FUNCTION OF GENOMIC DNA WITH ROBUST SIGNALS OF BIOCHEMICAL ACTIVITY: INTEGRATIVE GENOME-WIDE STUDIES OF ENHANCERS A Dissertation in Biochemistry, Microbiology and Molecular Biology by Nergiz Dogan © 2014 Nergiz Dogan Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2014 ii The dissertation of Nergiz Dogan was reviewed and approved* by the following: Ross C. Hardison T. Ming Chu Professor of Biochemistry and Molecular Biology Dissertation Advisor Chair of Committee David S. Gilmour Professor of Molecular and Cell Biology Anton Nekrutenko Professor of Biochemistry and Molecular Biology Robert F. Paulson Professor of Veterinary and Biomedical Sciences Philip Reno Assistant Professor of Antropology Scott B. Selleck Professor and Head of the Department of Biochemistry and Molecular Biology *Signatures are on file in the Graduate School iii ABSTRACT Genome-wide measurements of epigenetic features such as histone modifications, occupancy by transcription factors and coactivators provide the opportunity to understand more globally how genes are regulated. While much effort is being put into integrating the marks from various combinations of features, the contribution of each feature to accuracy of enhancer prediction is not known. We began with predictions of 4,915 candidate erythroid enhancers based on genomic occupancy by TAL1, a key hematopoietic transcription factor that is strongly associated with gene induction in erythroid cells. Seventy of these DNA segments occupied by TAL1 (TAL1 OSs) were tested by transient transfections of cultured hematopoietic cells, and 56% of these were active as enhancers. Sixty-six TAL1 OSs were evaluated in transgenic mouse embryos, and 65% of these were active enhancers in various tissues.
    [Show full text]
  • Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
    bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.
    [Show full text]
  • A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults
    A CLINICOPATHOLOGICAL AND MOLECULAR GENETIC ANALYSIS OF LOW-GRADE GLIOMA IN ADULTS Presented by ANUSHREE SINGH MSc A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy Brain Tumour Research Centre Research Institute in Healthcare Sciences Faculty of Science and Engineering University of Wolverhampton November 2014 i DECLARATION This work or any part thereof has not previously been presented in any form to the University or to any other body whether for the purposes of assessment, publication or for any other purpose (unless otherwise indicated). Save for any express acknowledgments, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. The right of Anushree Singh to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs and Patents Act 1988. At this date copyright is owned by the author. Signature: Anushree Date: 30th November 2014 ii ABSTRACT The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies.
    [Show full text]
  • Genetics of Familial Non-Medullary Thyroid Carcinoma (FNMTC)
    cancers Review Genetics of Familial Non-Medullary Thyroid Carcinoma (FNMTC) Chiara Diquigiovanni * and Elena Bonora Unit of Medical Genetics, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-051-208-8418 Simple Summary: Non-medullary thyroid carcinoma (NMTC) originates from thyroid follicular epithelial cells and is considered familial when occurs in two or more first-degree relatives of the patient, in the absence of predisposing environmental factors. Familial NMTC (FNMTC) cases show a high genetic heterogeneity, thus impairing the identification of pivotal molecular changes. In the past years, linkage-based approaches identified several susceptibility loci and variants associated with NMTC risk, however only few genes have been identified. The advent of next-generation sequencing technologies has improved the discovery of new predisposing genes. In this review we report the most significant genes where variants predispose to FNMTC, with the perspective that the integration of these new molecular findings in the clinical data of patients might allow an early detection and tailored therapy of the disease, optimizing patient management. Abstract: Non-medullary thyroid carcinoma (NMTC) is the most frequent endocrine tumor and originates from the follicular epithelial cells of the thyroid. Familial NMTC (FNMTC) has been defined in pedigrees where two or more first-degree relatives of the patient present the disease in absence of other predisposing environmental factors. Compared to sporadic cases, FNMTCs are often multifocal, recurring more frequently and showing an early age at onset with a worse outcome. FNMTC cases Citation: Diquigiovanni, C.; Bonora, E.
    [Show full text]
  • Statistical Methods for High-Dimensional Networked Data Analysis
    Statistical Methods for High-Dimensional Networked Data Analysis by Yan Zhou A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Biostatistics) in The University of Michigan 2015 Doctoral Committee: Professor Peter X. K. Song, Chair Professor Matthias Kretzler Assistant Professor Xiaoquan William Wen Professor Ji Zhu c Yan Zhou 2015 All Rights Reserved To my family ii ACKNOWLEDGEMENTS I would like to acknowledge many people for their help throughout my graduate study. I would first like to express my deepest gratitude to my advisor Dr. Peter X.- K. Song, for sharing his knowledge, encouraging me to work hard and constantly try to improve my work, and giving me the freedom to explore a diverse set of projects. He is the one who first sparked my interest in networked data analysis. I am not only respectful for his immense knowledge, motivation, and enthusiasm towards research, but also appreciate his invaluable guidance and enthusiastic encouragement during many difficult moments in my research. I could not have imagined having a better advisor and mentor for my Ph.D. study. I would also like to thank my other thesis committee members Dr. Ji Zhu, Dr. Xiaoquan Willian Wen and Dr. Matthias Kretzler for their help and advices that made me more productive than what I could achieve on my own. All enlightening discussions I had with them have lead to better contents of this dissertation. In addition, I would like to acknowledge my collaborators, Dr. Pei Wang, Dr. Betsy Lozoff and Dr. Fengji Geng, who offered me excellent opportunities and con- structive suggestions to apply my statistical knowledge to solve real-world problems, which greatly strengthened the scientific background of my dissertation research.
    [Show full text]
  • Glucose-Induced Changes in Gene Expression in Human Pancreatic Islets: Causes Or Consequences of Chronic Hyperglycemia
    Diabetes Volume 66, December 2017 3013 Glucose-Induced Changes in Gene Expression in Human Pancreatic Islets: Causes or Consequences of Chronic Hyperglycemia Emilia Ottosson-Laakso,1 Ulrika Krus,1 Petter Storm,1 Rashmi B. Prasad,1 Nikolay Oskolkov,1 Emma Ahlqvist,1 João Fadista,2 Ola Hansson,1 Leif Groop,1,3 and Petter Vikman1 Diabetes 2017;66:3013–3028 | https://doi.org/10.2337/db17-0311 Dysregulation of gene expression in islets from patients In patients with type 2 diabetes (T2D), islet function de- with type 2 diabetes (T2D) might be causally involved clines progressively. Although the initial pathogenic trigger in the development of hyperglycemia, or it could develop of impaired b-cell function is still unknown, elevated glu- as a consequence of hyperglycemia (i.e., glucotoxicity). cose levels are known to further aggravate b-cell function, a To separate the genes that could be causally involved condition referred to as glucotoxicity, which can stimulate in pathogenesis from those likely to be secondary to hy- apoptosis and lead to reduced b-cell mass (1–5). Prolonged perglycemia, we exposed islets from human donors to exposure to hyperglycemia also can induce endoplasmic re- ISLET STUDIES normal or high glucose concentrations for 24 h and ana- ticulum (ER) stress and production of reactive oxygen spe- fi lyzed gene expression. We compared these ndings with cies (6), which can further impair islet function and thereby gene expression in islets from donors with normal glucose the ability of islets to secrete the insulin needed to meet the tolerance and hyperglycemia (including T2D). The genes increased demands imposed by insulin resistance and obe- whose expression changed in the same direction after sity (7).
    [Show full text]
  • 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,
    [Show full text]