An Ultraconserved Element Controls Homeostatic Splicing of ARGLU1 Mrna Stephan P
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Genome-Wide Association Study for Circulating Fibroblast Growth Factor
www.nature.com/scientificreports OPEN Genome‑wide association study for circulating fbroblast growth factor 21 and 23 Gwo‑Tsann Chuang1,2,14, Pi‑Hua Liu3,4,14, Tsui‑Wei Chyan5, Chen‑Hao Huang5, Yu‑Yao Huang4,6, Chia‑Hung Lin4,7, Jou‑Wei Lin8, Chih‑Neng Hsu8, Ru‑Yi Tsai8, Meng‑Lun Hsieh9, Hsiao‑Lin Lee9, Wei‑shun Yang2,9, Cassianne Robinson‑Cohen10, Chia‑Ni Hsiung11, Chen‑Yang Shen12,13 & Yi‑Cheng Chang2,9,12* Fibroblast growth factors (FGFs) 21 and 23 are recently identifed hormones regulating metabolism of glucose, lipid, phosphate and vitamin D. Here we conducted a genome‑wide association study (GWAS) for circulating FGF21 and FGF23 concentrations to identify their genetic determinants. We enrolled 5,000 participants from Taiwan Biobank for this GWAS. After excluding participants with diabetes mellitus and quality control, association of single nucleotide polymorphisms (SNPs) with log‑transformed FGF21 and FGF23 serum concentrations adjusted for age, sex and principal components of ancestry were analyzed. A second model additionally adjusted for body mass index (BMI) and a third model additionally adjusted for BMI and estimated glomerular fltration rate (eGFR) were used. A total of 4,201 participants underwent GWAS analysis. rs67327215, located within RGS6 (a gene involved in fatty acid synthesis), and two other SNPs (rs12565114 and rs9520257, located between PHC2-ZSCAN20 and ARGLU1-FAM155A respectively) showed suggestive associations with serum FGF21 level (P = 6.66 × 10–7, 6.00 × 10–7 and 6.11 × 10–7 respectively). The SNPs rs17111495 and rs17843626 were signifcantly associated with FGF23 level, with the former near PCSK9 gene and the latter near HLA-DQA1 gene (P = 1.04 × 10–10 and 1.80 × 10–8 respectively). -
Controls Homeostatic Splicing of ARGLU1 Mrna Stephan P
Published online 28 November 2016 Nucleic Acids Research, 2017, Vol. 45, No. 6 3473–3486 doi: 10.1093/nar/gkw1140 An Ultraconserved Element (UCE) controls homeostatic splicing of ARGLU1 mRNA Stephan P. Pirnie, Ahmad Osman, Yinzhou Zhu and Gordon G. Carmichael* Department of Genetics and Genome Sciences, UCONN Health Center, 400 Farmington Avenue, Farmington, CT 06030, USA Received January 13, 2016; Revised October 25, 2016; Editorial Decision October 28, 2016; Accepted October 31, 2016 ABSTRACT or inhibit the usage of a particular splice site (1,2). The ex- pression of trans-acting factors in a developmental and tis- Arginine and Glutamate-Rich protein 1 (ARGLU1) is sue specific manner results in regulated splicing that isof- a protein whose function is poorly understood, but ten cell specific. Most notably, trans-acting proteins such as may act in both transcription and pre-mRNA splicing. the NOVA (3–6), the RBFOX (7) and SR protein (8–11) We demonstrate that the ARGLU1 gene expresses families, and a number of hnRNP (12,13) proteins compete at least three distinct RNA splice isoforms – a fully to bind nascent RNAs at specific motifs and drive regula- spliced isoform coding for the protein, an isoform tion of alternative splicing in a tissue and developmentally containing a retained intron that is detained in the specific manner. Alternative splicing is an important pro- nucleus, and an isoform containing an alternative cess that is seen in at least 95% of multi-exon genes in the exon that targets the transcript for nonsense medi- human transcriptome (14). Furthermore, alternative splic- ated decay. -
Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage. -
S41467-020-18249-3.Pdf
ARTICLE https://doi.org/10.1038/s41467-020-18249-3 OPEN Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain Lei Zhao1,2,17, Zhongqi Li 1,2,17, Joaquim S. L. Vong2,3,17, Xinyi Chen1,2, Hei-Ming Lai1,2,4,5,6, Leo Y. C. Yan1,2, Junzhe Huang1,2, Samuel K. H. Sy1,2,7, Xiaoyu Tian 8, Yu Huang 8, Ho Yin Edwin Chan5,9, Hon-Cheong So6,8, ✉ ✉ Wai-Lung Ng 10, Yamei Tang11, Wei-Jye Lin12,13, Vincent C. T. Mok1,5,6,14,15 &HoKo 1,2,4,5,6,8,14,16 1234567890():,; The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovas- cular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation- dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which pro- minently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood–brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed tran- scriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible. -
Methods in and Applications of the Sequencing of Short Non-Coding Rnas" (2013)
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Methods in and Applications of the Sequencing of Short Non- Coding RNAs Paul Ryvkin University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Molecular Biology Commons Recommended Citation Ryvkin, Paul, "Methods in and Applications of the Sequencing of Short Non-Coding RNAs" (2013). Publicly Accessible Penn Dissertations. 922. https://repository.upenn.edu/edissertations/922 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/922 For more information, please contact [email protected]. Methods in and Applications of the Sequencing of Short Non-Coding RNAs Abstract Short non-coding RNAs are important for all domains of life. With the advent of modern molecular biology their applicability to medicine has become apparent in settings ranging from diagonistic biomarkers to therapeutics and fields angingr from oncology to neurology. In addition, a critical, recent technological development is high-throughput sequencing of nucleic acids. The convergence of modern biotechnology with developments in RNA biology presents opportunities in both basic research and medical settings. Here I present two novel methods for leveraging high-throughput sequencing in the study of short non- coding RNAs, as well as a study in which they are applied to Alzheimer's Disease (AD). The computational methods presented here include High-throughput Annotation of Modified Ribonucleotides (HAMR), which enables researchers to detect post-transcriptional covalent modifications ot RNAs in a high-throughput manner. In addition, I describe Classification of RNAs by Analysis of Length (CoRAL), a computational method that allows researchers to characterize the pathways responsible for short non-coding RNA biogenesis. -
Meta-Analysis of Genome-Wide Association Studies for Personality
Molecular Psychiatry (2012) 17, 337–349 & 2012 Macmillan Publishers Limited All rights reserved 1359-4184/12 www.nature.com/mp ORIGINAL ARTICLE Meta-analysis of genome-wide association studies for personality MHM de Moor1, PT Costa2, A Terracciano2, RF Krueger3, EJC de Geus1, T Toshiko2, BWJH Penninx4,5,6, T Esko7,8,9, PAF Madden10, J Derringer3, N Amin11, G Willemsen1, J-J Hottenga1, MA Distel1, M Uda12, S Sanna12, P Spinhoven5, CA Hartman4, P Sullivan13, A Realo14, J Allik14, AC Heath10, ML Pergadia10, A Agrawal10, P Lin10, R Grucza10, T Nutile15, M Ciullo15, D Rujescu16, I Giegling16, B Konte16, E Widen17, DL Cousminer17, JG Eriksson18,19,20,21,22, A Palotie17,23,24,25, L Peltonen17,23,24,25,{, M Luciano26, A Tenesa27, G Davies26, LM Lopez26, NK Hansell28, SE Medland28, L Ferrucci2, D Schlessinger2, GW Montgomery28, MJ Wright28, YS Aulchenko11, ACJW Janssens11, BA Oostra29, A Metspalu7,8,9, GR Abecasis30, IJ Deary26,KRa¨ikko¨nen31, LJ Bierut10, NG Martin28, CM van Duijn11,32 and DI Boomsma1,32 1Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands; 2National Institute on Aging, NIH, Baltimore, MD, USA; 3Department of Psychology, University of Minnesota, Minneapolis, MN, USA; 4Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands; 5Departments of Clinical Psychology and Psychiatry, Leiden University, Leiden, The Netherlands; 6Department of Psychiatry, EMGO þ Institute, Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands; -
Genomic and Transcriptomic Investigations Into the Feed Efficiency Phenotype of Beef Cattle
Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published version when available. Title Genomic and transcriptomic investigations into the feed efficiency phenotype of beef cattle Author(s) Higgins, Marc Publication Date 2019-03-06 Publisher NUI Galway Item record http://hdl.handle.net/10379/15008 Downloaded 2021-09-25T18:07:39Z Some rights reserved. For more information, please see the item record link above. Genomic and Transcriptomic Investigations into the Feed Efficiency Phenotype of Beef Cattle Marc Higgins, B.Sc., M.Sc. A thesis submitted for the Degree of Doctor of Philosophy to the Discipline of Biochemistry, School of Natural Sciences, National University of Ireland, Galway. Supervisor: Dr. Derek Morris Discipline of Biochemistry, School of Natural Sciences, National University of Ireland, Galway. Supervisor: Dr. Sinéad Waters Teagasc, Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange. Submitted November 2018 Table of Contents Declaration ................................................................................................................ vii Funding .................................................................................................................... viii Acknowledgements .................................................................................................... ix Abstract ...................................................................................................................... -
Découverte De Nouveaux Transcrits De Fusion Dans Des Tumeurs
Découverte de nouveaux transcrits de fusion dans des tumeurs pédiatriques en rechute et caractérisation fonctionnelle d’un nouvel oncogène LMO3-BORCS5 Celia Dupain Jourda To cite this version: Celia Dupain Jourda. Découverte de nouveaux transcrits de fusion dans des tumeurs pédiatriques en rechute et caractérisation fonctionnelle d’un nouvel oncogène LMO3-BORCS5. Cancer. Université Paris Saclay (COmUE), 2018. Français. NNT : 2018SACLS310. tel-02426230 HAL Id: tel-02426230 https://tel.archives-ouvertes.fr/tel-02426230 Submitted on 2 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Découverte de nouveaux transcrits de fusion dans des tumeurs pédiatriques en rechute et caractérisation fonctionnelle d’un nouvel oncogène LMO3-BORCS5 NNT: 2018SACLS310 NNT: Thèse de doctorat de l'Université Paris-Saclay préparée à l’Université Paris-Sud École doctorale n°568 : Signalisations et réseaux intégratifs en biologie (Biosigne) Spécialité de doctorat: Aspects moléculaires et cellulaires de la biologie Thèse présentée et soutenue à Villejuif, le 16 octobre -
Alternative Splicing in the Nuclear Receptor Superfamily Expands Gene Function to Refine Endo-Xenobiotic Metabolism S
Supplemental material to this article can be found at: http://dmd.aspetjournals.org/content/suppl/2020/01/24/dmd.119.089102.DC1 1521-009X/48/4/272–287$35.00 https://doi.org/10.1124/dmd.119.089102 DRUG METABOLISM AND DISPOSITION Drug Metab Dispos 48:272–287, April 2020 Copyright ª 2020 by The American Society for Pharmacology and Experimental Therapeutics Minireview Alternative Splicing in the Nuclear Receptor Superfamily Expands Gene Function to Refine Endo-Xenobiotic Metabolism s Andrew J. Annalora, Craig B. Marcus, and Patrick L. Iversen Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon (A.J.A., C.B.M., P.L.I.) and United States Army Research Institute for Infectious Disease, Frederick, Maryland (P.L.I.) Received August 16, 2019; accepted December 31, 2019 ABSTRACT Downloaded from The human genome encodes 48 nuclear receptor (NR) genes, whose Exon inclusion options are differentially distributed across NR translated products transform chemical signals from endo- subfamilies, suggesting group-specific conservation of resilient func- xenobiotics into pleotropic RNA transcriptional profiles that refine tionalities. A deeper understanding of this transcriptional plasticity drug metabolism. This review describes the remarkable diversifica- expands our understanding of how chemical signals are refined and tion of the 48 human NR genes, which are potentially processed into mediated by NR genes. This expanded view of the NR transcriptome dmd.aspetjournals.org over 1000 distinct mRNA transcripts by alternative splicing (AS). The informs new models of chemical toxicity, disease diagnostics, and average human NR expresses ∼21 transcripts per gene and is precision-based approaches to personalized medicine. -
Meta-Analysis of Genome-Wide Association Studies for Personality Europe PMC Funders Group
Europe PMC Funders Group Author Manuscript Mol Psychiatry. Author manuscript; available in PMC 2013 September 27. Published in final edited form as: Mol Psychiatry. 2012 March ; 17(3): 337–349. doi:10.1038/mp.2010.128. Europe PMC Funders Author Manuscripts Meta-analysis of genome-wide association studies for personality Marleen H.M. de Moor1,†, Paul T. Costa2, Antonio Terracciano2, Robert F. Krueger3, Eco J.C. de Geus1, Tanaka Toshiko2, Brenda W.J.H. Penninx4,5,6, Tõnu Esko7,8,9, Pamela A F Madden10, Jaime Derringer3, Najaf Amin11, Gonneke Willemsen1, Jouke-Jan Hottenga1, Marijn A. Distel1, Manuela Uda12, Serena Sanna12, Philip Spinhoven5, Catharina A. Hartman4, Patrick Sullivan13, Anu Realo14, Jüri Allik14, Andrew C Heath10, Michele L Pergadia10, Arpana Agrawal10, Peng Lin10, Richard Grucza10, Teresa Nutile15, Marina Ciullo15, Dan Rujescu16, Ina Giegling16, Bettina Konte16, Elisabeth Widen17, Diana L Cousminer17, Johan G. Eriksson18,19,20,21,22, Aarno Palotie17,23,24,31, Leena Peltonen17,23,24,31,**, Michelle Luciano25, Albert Tenesa26, Gail Davies25, Lorna M. Lopez25, Narelle K. Hansell27, Sarah E. Medland27, Luigi Ferrucci2, David Schlessinger2, Grant W. Montgomery27, Margaret J. Wright27, Yurii S. Aulchenko11, A.Cecile J.W. Janssens11, Ben A. Oostra28, Andres Metspalu7,8,9, Gonçalo R. Abecasis29, Ian J. Deary25, Katri Räikkönen30, Laura J. Bierut10, Nicholas G. Martin27, Cornelia M. van Duijn11,*, and Dorret I. Boomsma1,* 1Department of Biological Psychology, VU University Amsterdam, 1081 BT, Amsterdam, The Netherlands 2National Institute -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19