DOCSLIB.ORG

The Regulation of Htert by Alternative Splicing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Regulation of Htert by Alternative Splicing THE REGULATION OF HTERT BY ALTERNATIVE SPLICING APPROVED BY SUPERVISORY COMMITTEE First Name Last Name, credentials Jerry Shay, Ph.D. Woodring Wright, M.D., Ph.D. Hongtao Yu, Ph.D. Melanie Cobb, Ph.D. Beatriz Fontoura, Ph.D. DEDICATION I would like to thank the members of my Graduate Committee and mentors, Jerry Shay and Woodring Wright, all of the members of the Shay/Wright lab, my collaborators, the UTSW MSTP, my friends, my family, my significant other and my two cats, Kerrigan and Tofu. THE REGULATION OF HTERT BY ALTERLATIVE SPLICING by LAURA YU YUAN DISSERTATION Presented to the Faculty of the Graduate School of Biomedical Sciences The University of Texas Southwestern Medical Center at Dallas In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY The University of Texas Southwestern Medical Center at Dallas Dallas, Texas May, 2019 Copyright by Laura Yu Yuan, 2019 All Rights Reserved THE REGULATION OF HTERT BY ALTERNATIVE SPLICING Publication No. Laura Yu Yuan, M.D., Ph.D. The University of Texas Southwestern Medical Center at Dallas, 2019 Supervising Professors: Jerry Shay, Ph.D., and Woodring Wright, M.D., Ph.D. Telomeres are non-coding DNA hexameric repeats (TTAGGG in mammals) located at the ends of linear chromosomes that, along with their associated proteins, protect against the loss of genomic material during cell division and prevent the recognition of chromosome ends as double-strand breaks. Human telomeres shorten with continued cell proliferation but are maintained by human telomerase reverse transcriptase (hTERT), an enzyme that synthesizes telomeric repeats using an RNA template. The regulation of telomerase has been studied at many levels—from epigenetic and transcriptional regulation to the alternative splicing of hTERT pre-mRNA into catalytically ixv inactive splice variants. Our hypothesis is that if the regulation of telomerase reverse transcriptase splicing is necessary for telomere length homeostasis, altering telomerase splicing to decrease the production of full-length hTERT and will result in decreased telomerase activity and subsequently telomere shortening. We focused our efforts on identifying splicing factors are involved in hTERT splicing and characterized the role of two splicing factors, NOVA1 and PTBP1, in regulation of hTERT splicing in non-small cell lung cancer cells. We show that these splicing factors are important for full-length hTERT, telomerase activity and telomere length maintenance in vitro. Xenograft studies suggest that NOVA1 is also important for tumor growth in vivo. We found that these splicing factors are able to directly interact with hTERT in a region our group previously identified to be important for hTERT splicing. Altogether, our work suggests that splicing factors are important for hTERT regulation and telomerase activity in cancer. Since telomerase activity is undetectable in most somatic tissues but is increased in the vast majority of human cancers, dependence on telomerase represents a key vulnerability in cancer tissues which could be therapeutically targetable. viiivi TABLE OF CONTENTS ABSTRACT ........................................................................................................................... v CHAPTER ONE: INTRODUCTION .................................................................................... 1 INTRODUCTION TO TELOMERES AND TELOMERASE ....................................... 1 TELOMERASE REVERSE TRANSCRIPTASE ........................................................... 4 TERT REGULATION ..................................................................................................... 6 CHAPTER TWO: REVIEW OF LITERATURE ................................................................ 13 INTRODUCTION TO SPLICING ................................................................................ 13 MUTATED SPLICING FACTORS AS ONCOGENES ................................................ 15 DYSREGULATED SPLICING FACTORS AS ONCOGENES .................................. 19 SPLICING FACTORS AS TUMOR SUPPRESSORS ................................................. 22 OTHER REGULATORY FACTORS ............................................................................ 24 CHAPTER THREE: METHODS AND MATERIALS ...................................................... 27 CHAPTER FOUR: NOVA1 RESULTS .............................................................................. 39 BACKGROUND ............................................................................................................ 39 RESULTS ....................................................................................................................... 40 DISCUSSION ................................................................................................................. 70 CHAPTER FIVE: PTBP1 RESULTS ................................................................................. 72 BACKGROUND ............................................................................................................ 72 RESULTS ....................................................................................................................... 73 DISCUSSION ................................................................................................................. 81 CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS ................................... 83 viiix CHAPTER SEVEN: BIBLIOGRAPHY .............................................................................. 98 viii PRIOR PUBLICATIONS Gao, Z., Ure, K., Ding, P., Nashaat, M., Yuan, L., Ma, J., Hammer, R. E., & Hsieh, J. (2011). The master negative regulator REST/NRSF controls adult neurogenesis by restraining the neurogenic program in quiescent stem cells. J Neurosci.,31 (26), 9772-86. ix LIST OF FIGURES FIGURE ONE ......................................................................................................................... 2 FIGURE TWO ....................................................................................................................... 3 FIGURE THREE ................................................................................................................... 5 FIGURE FOUR ................................................................................................................... 15 FIGURE FIVE ..................................................................................................................... 16 FIGURE SIX ........................................................................................................................ 17 FIGURE SEVEN ................................................................................................................. 41 FIGURE EIGHT .................................................................................................................. 50 FIGURE NINE ..................................................................................................................... 53 FIGURE TEN ...................................................................................................................... 58 FIGURE ELEVEN ............................................................................................................... 61 FIGURE TWELVE .............................................................................................................. 66 FIGURE THIRTEEN ........................................................................................................... 69 FIGURE FOURTEEN ......................................................................................................... 73 FIGURE FIFTEEN .............................................................................................................. 74 FIGURE SIXTEEN ............................................................................................................. 75 FIGURE SEVENTEEN ....................................................................................................... 76 FIGURE EIGHTEEN .......................................................................................................... 76 FIGURE NINETEEN .......................................................................................................... 78 FIGURE TWENTY ............................................................................................................. 78 FIGURE TWENTY-ONE .................................................................................................... 79 ixx FIGURE TWENTY-TWO ................................................................................................... 80 FIGURE TWENTY-THREE ............................................................................................... 81 FIGURE TWENTY-FOUR ................................................................................................. 89 viiixi LIST OF TABLES TABLE ONE ....................................................................................................................... 90 TABLE TWO ....................................................................................................................... 91 xiiix LIST OF APPENDICES APPENDIX ONE ................................................................................................................. 90 xiiiviii LIST OF DEFINITIONS ATM – ataxia-telangiectasia mutated Bcl-x – B-cell lymphoma x CDC40 - cell division cycle 40 CLIP – cross-linking immunoprecipitation CLIP-ddPCR – crosslinking immunoprecipitation followed by RT-ddPCR CLK – CDC
Load more

Recommended publications
  • BIOINFORMATICS DOI: 10.1093/Bioinformatics/Btg1030
    Vol. 19 Suppl. 1 2003, pages i222–i224 BIOINFORMATICS DOI: 10.1093/bioinformatics/btg1030 GeneLoc: exon-based integration of human genome maps Naomi Rosen, Vered Chalifa-Caspi, Orit Shmueli, Avital Adato, Michal Lapidot, Julie Stampnitzky, Marilyn Safran ∗ and Doron Lancet Weizmann Institute of Science, Rehovot, Israel Received on January 6, 2003; accepted on February 20, 2003 ABSTRACT to provide a comprehensive gene list, NCBI’s LocusLink Motivation: Despite the numerous available whole- contains thousands of model genes, categorized by level genome mapping resources, no comprehensive, inte- and type of support. Even known genes appearing in every grated map of the human genome yet exists. database may have different names in each database. The Results: GeneLoc, software adjunct to GeneCards and biologist must move among databases to figure out which UDB, integrates gene lists by comparing genomic coordi- genes are the same, and which could be a novel gene nates at the exon level and assigns unique and meaningful sought. UCSC’s Genome Browser website maps genes identifiers to each gene. from several sources on the same scale, but the maps are Availability: http://bioinfo.weizmann.ac.il/genecards and not integrated, making it difficult to relate genes from http://genecards.weizmann.ac.il/udb different sources. As stated (Jongeneel, 2000),‘there is an Supplementary information: http://bioinfo.weizmann.ac. urgent need for a human gene index that can be used to il/cards-bin/AboutGCids.cgi, http://genecards.weizmann. identify transcripts unambiguously.’ The author contends ac.il/GeneLocAlg.html that this index should have, among others, the following Contact: [email protected] qualities: comprehensiveness, uniqueness, and stability.
    [Show full text]
  • Genome Analysis and Knowledge
    Dahary et al. BMC Medical Genomics (2019) 12:200 https://doi.org/10.1186/s12920-019-0647-8 SOFTWARE Open Access Genome analysis and knowledge-driven variant interpretation with TGex Dvir Dahary1*, Yaron Golan1, Yaron Mazor1, Ofer Zelig1, Ruth Barshir2, Michal Twik2, Tsippi Iny Stein2, Guy Rosner3,4, Revital Kariv3,4, Fei Chen5, Qiang Zhang5, Yiping Shen5,6,7, Marilyn Safran2, Doron Lancet2* and Simon Fishilevich2* Abstract Background: The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient’s phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. Results: We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex’s main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex’s broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards’ recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters.
    [Show full text]
  • Full Review Molecular Disease Presentation in Diabetic Nephropathy
    Nephrol Dial Transplant (2015) 30: iv17–iv25 doi: 10.1093/ndt/gfv267 Full Review Molecular disease presentation in diabetic nephropathy Downloaded from https://academic.oup.com/ndt/article/30/suppl_4/iv17/2324968 by guest on 25 September 2021 Andreas Heinzel1, Irmgard Mühlberger1, Gil Stelzer2, Doron Lancet2, Rainer Oberbauer3, Maria Martin4 and Paul Perco1 1emergentec biodevelopment GmbH, Vienna, Austria, 2Weizmann Institute of Science, Rehovot, Israel, 3Medical University of Vienna, Vienna, Austria and 4EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, UK Correspondence and offprint requests to: Paul Perco; E-mail: [email protected] ABSTRACT INTRODUCTION Diabetic nephropathy, as the most prevalent chronic disease of Kidney disease is a common problem in patients with diabetes. the kidney, has also become the primary cause of end-stage Depending on the degree of GFR impairment and/or albumin- renal disease with the incidence of kidney disease in type 2 dia- uria, roughly 30% of subjects are affected. As with most betics continuously rising. As with most chronic diseases, the diabetes-associated comorbidities, the pathophysiology is pathophysiology is multifactorial with a number of deregulated multifactorial and the molecular pathways involved in the ini- molecular processes contributing to disease manifestation and tiation and progression constitute a wide and complex as well as progression. Current therapy mainly involves interfering in the redundant network of regulators. Considerable success has renin–angiotensin–aldosterone
    [Show full text]
  • Causal Varian Discovery in Familial Congenital Heart Disease - an Integrative -Omic Approach Wendy Demos Marquette University
    Marquette University e-Publications@Marquette Master's Theses (2009 -) Dissertations, Theses, and Professional Projects Causal Varian discovery in Familial Congenital Heart Disease - An Integrative -Omic Approach Wendy Demos Marquette University Recommended Citation Demos, Wendy, "Causal Varian discovery in Familial Congenital Heart Disease - An Integrative -Omic Approach" (2012). Master's Theses (2009 -). 140. https://epublications.marquette.edu/theses_open/140 CAUSAL VARIANT DISCOVERY IN FAMILIAL CONGENITAL HEART DISEASE – AN INTEGRATIVE –OMIC APPROACH by Wendy M. Demos A Thesis submitted to the Faculty of the Graduate School, Marquette University, in Partial Fulfillment of the Requirements for the Degree of Master of Science Milwaukee, Wisconsin May 2012 ABSTRACT CAUSAL VARIANT DISCOVERY IN FAMILIAL CONGENITAL HEART DISEASE – AN INTEGRATIVE –OMIC APPROACH Wendy M. Demos Marquette University, 2012 Background : Hypoplastic left heart syndrome (HLHS) is a congenital heart defect that leads to neonatal death or compromised quality of life for those affected and their families. This syndrome requires extensive medical intervention for the affected to survive. It is characterized by significant underdevelopment or non-existence of the components of the left heart and the aorta, including the left ventricular cavity and mass. There are many factors ranging from genetics to environmental relationships hypothesized to lead to the development of the syndrome, including recent studies suggesting a link between hearing impairment and congenital heart defects (CHD). Although broadly characterized those factors remain poorly understood. The goal of this project is to systematically utilize bioinformatics tools to determine the relationships of novel mutations found in exome sequencing to a familial congenital heart defect. Methods A systematic genomic and proteomic approach involving exome sequencing, pathway analysis, and protein modeling was implemented to examine exome sequencing data of a patient with HLHS.
    [Show full text]
  • A New Cerkl Mouse Model Generated by CRISPR-Cas9 Shows
    Retina ANewCerkl Mouse Model Generated by CRISPR-Cas9 Shows Progressive Retinal Degeneration and Altered Morphological and Electrophysiological Phenotype Elena B. Domènech,1,2 Rosa Andrés,1,2 M. José López-Iniesta,1 Serena Mirra,1,2 Rocío García-Arroyo,1 Santiago Milla,3 Florentina Sava,1 Jordi Andilla,4 Pablo Loza-Álvarez,4 Pedro de la Villa,3,5 Roser Gonzàlez-Duarte,1,2,6 and Gemma Marfany1,2,6,7 1Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain 2CIBERER/ISCIII, University of Barcelona, Barcelona, Spain 3Department of Systems Biology, University of Alcalá, Madrid, Spain 4ICFO–The Institute of Photonic Sciences, Barcelona Institute of Science and Technology, Barcelona, Spain 5Ramón y Cajal Institute for Health Research, Madrid, Spain 6DBGen Ocular Genomics, Barcelona, Spain 7Institute of Biomedicine, University of Barcelona, Barcelona, Spain Correspondence: Gemma Marfany, PURPOSE. Close to 100 genes cause retinitis pigmentosa, a Mendelian rare disease that Avinguda Diagonal, 643, edifici affects 1 out of 4000 people worldwide. Mutations in the ceramide kinase-like gene Prevosti, planta 1, 08028 Barcelona, (CERKL) are a prevalent cause of autosomal recessive cause retinitis pigmentosa and Spain; cone–rod dystrophy, but the functional role of this gene in the retina has yet to be fully [email protected]. determined. We aimed to generate a mouse model that resembles the phenotypic traits of Received: January 20, 2020 patients carrying CERKL mutations to undertake functional studies and assay therapeutic Accepted: June 14, 2020 approaches. Published: July 13, 2020 METHODS. The Cerkl locus has been deleted (around 97 kb of genomic DNA) by gene Citation: Domènech EB, Andrés R, editing using the CRISPR-Cas9 D10A nickase.
    [Show full text]
  • 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.
    [Show full text]
  • Identification of Differentially Expressed Genes in Human Bladder Cancer Through Genome-Wide Gene Expression Profiling
    521-531 24/7/06 18:28 Page 521 ONCOLOGY REPORTS 16: 521-531, 2006 521 Identification of differentially expressed genes in human bladder cancer through genome-wide gene expression profiling KAZUMORI KAWAKAMI1,3, HIDEKI ENOKIDA1, TOKUSHI TACHIWADA1, TAKENARI GOTANDA1, KENGO TSUNEYOSHI1, HIROYUKI KUBO1, KENRYU NISHIYAMA1, MASAKI TAKIGUCHI2, MASAYUKI NAKAGAWA1 and NAOHIKO SEKI3 1Department of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima 890-8520; Departments of 2Biochemistry and Genetics, and 3Functional Genomics, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Japan Received February 15, 2006; Accepted April 27, 2006 Abstract. Large-scale gene expression profiling is an effective CKS2 gene not only as a potential biomarker for diagnosing, strategy for understanding the progression of bladder cancer but also for staging human BC. This is the first report (BC). The aim of this study was to identify genes that are demonstrating that CKS2 expression is strongly correlated expressed differently in the course of BC progression and to with the progression of human BC. establish new biomarkers for BC. Specimens from 21 patients with pathologically confirmed superficial (n=10) or Introduction invasive (n=11) BC and 4 normal bladder samples were studied; samples from 14 of the 21 BC samples were subjected Bladder cancer (BC) is among the 5 most common to microarray analysis. The validity of the microarray results malignancies worldwide, and the 2nd most common tumor of was verified by real-time RT-PCR. Of the 136 up-regulated the genitourinary tract and the 2nd most common cause of genes we detected, 21 were present in all 14 BCs examined death in patients with cancer of the urinary tract (1-7).
    [Show full text]
  • "The Genecards Suite: from Gene Data Mining to Disease Genome Sequence Analyses". In: Current Protocols in Bioinformat
    The GeneCards Suite: From Gene Data UNIT 1.30 Mining to Disease Genome Sequence Analyses Gil Stelzer,1,5 Naomi Rosen,1,5 Inbar Plaschkes,1,2 Shahar Zimmerman,1 Michal Twik,1 Simon Fishilevich,1 Tsippi Iny Stein,1 Ron Nudel,1 Iris Lieder,2 Yaron Mazor,2 Sergey Kaplan,2 Dvir Dahary,2,4 David Warshawsky,3 Yaron Guan-Golan,3 Asher Kohn,3 Noa Rappaport,1 Marilyn Safran,1 and Doron Lancet1,6 1Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel 2LifeMap Sciences Ltd., Tel Aviv, Israel 3LifeMap Sciences Inc., Marshfield, Massachusetts 4Toldot Genetics Ltd., Hod Hasharon, Israel 5These authors contributed equally to the paper 6Corresponding author GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It au- tomatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. Var- Elect’s capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses.
    [Show full text]
  • Telomerase a Prognostic Marker and Therapeutic Target
    Telomerase a prognostic marker and therapeutic target By Dipti S. Thakkar A thesis submitted to the University of Central Lancashire in partial fulfilment of the requirements for the degree of Doctor of Philosophy Month Year submitted: June 2010 1 Declaration I declare that while registered as a candidate for this degree I have not been registered as a candidate for any other award from an academic institution. The work present in this thesis, except where otherwise stated, is based on my own research and has not been submitted previously for any other award in this or any other University. DIPTI THAKKAR Signed 2 Abstract Malignant glioma is the most common and aggressive form of tumours and is usually refractory to therapy. Telomerase and its altered activity, distinguishing cancer cells, is an attractive molecular target in glioma therapeutics. The aim of this thesis was to silence telomerase at the genetic level with a view to highlight the changes caused in the cancer proteome and identify the potential downstream pathways controlled by telomerase in tumour progression and maintenance. A comprehensive proteomic study utilizing 2D-DIGE and MALDI-TOF were used to assess the effect of inhibiting two different regulatory mechanisms of telomerase in glioma. RNAi was used to target hTERT and Hsp90α. Inhibition of telomerase activity resulted in down regulation of various cytoskeletal proteins with correlative evidence of the involvement of telomerase in regulating the expression of vimentin. Vimentin plays an important role in tumour metastasis and is used as an indicator of glioma metastasis. Inhibition of telomerase via sihTERT results in the down regulation of vimentin expression in glioma cell lines in a grade specific manner.
    [Show full text]
  • GCH1 Variants Contribute to the Risk and Earlier Age-At-Onset Of
    Pan et al. Translational Neurodegeneration (2020) 9:31 https://doi.org/10.1186/s40035-020-00212-3 RESEARCH Open Access GCH1 variants contribute to the risk and earlier age-at-onset of Parkinson’s disease: a two-cohort case-control study Hong-xu Pan1,2†, Yu-wen Zhao1,2†, Jun-pu Mei1, Zheng-huan Fang3, Yige Wang1, Xun Zhou1, Yang-jie Zhou1, Rui Zhang1, Kai-lin Zhang1, Li Jiang1, Qian Zeng1, Yan He1, Zheng Wang4, Zhen-hua Liu1, Qian Xu1, Qi-ying Sun4, Yang Yang1, Ya-cen Hu4, Ya-se Chen1, Juan Du1, Li-fang Lei5, Hai-nan Zhang6, Chun-yu Wang6, Xin-xiang Yan1, Lu Shen1,2,7, Hong Jiang1,2,7, Jie-qiong Tan3, Jin-chen Li2,3,7, Bei-sha Tang1,2,3,7* and Ji-feng Guo1,2,3,7* Abstract Background: Common and rare variants of guanosine triphosphate cyclohydrolase 1 (GCH1) gene may play important roles in Parkinson’s disease (PD). However, there is a lack of comprehensive analysis of GCH1 genotypes, especially in non-coding regions. The aim of this study was to explore the genetic characteristics of GCH1, including rare and common variants in coding and non-coding regions, in a large population of PD patients in Chinese mainland, as well as the phenotypic characteristics of GCH1 variant carriers. Methods: In the first cohort of this case-control study, we performed whole-exome sequencing in 1555 patients with early-onset or familial PD and 2234 healthy controls; then in the second cohort, whole-genome sequencing was performed in sporadic late-onset PD samples (1962 patients), as well as 1279 controls.
    [Show full text]
  • Skeletal Muscle Transcriptome in Healthy Aging
    ARTICLE https://doi.org/10.1038/s41467-021-22168-2 OPEN Skeletal muscle transcriptome in healthy aging Robert A. Tumasian III 1, Abhinav Harish1, Gautam Kundu1, Jen-Hao Yang1, Ceereena Ubaida-Mohien1, Marta Gonzalez-Freire1, Mary Kaileh1, Linda M. Zukley1, Chee W. Chia1, Alexey Lyashkov1, William H. Wood III1, ✉ Yulan Piao1, Christopher Coletta1, Jun Ding1, Myriam Gorospe1, Ranjan Sen1, Supriyo De1 & Luigi Ferrucci 1 Age-associated changes in gene expression in skeletal muscle of healthy individuals reflect accumulation of damage and compensatory adaptations to preserve tissue integrity. To characterize these changes, RNA was extracted and sequenced from muscle biopsies col- 1234567890():,; lected from 53 healthy individuals (22–83 years old) of the GESTALT study of the National Institute on Aging–NIH. Expression levels of 57,205 protein-coding and non-coding RNAs were studied as a function of aging by linear and negative binomial regression models. From both models, 1134 RNAs changed significantly with age. The most differentially abundant mRNAs encoded proteins implicated in several age-related processes, including cellular senescence, insulin signaling, and myogenesis. Specific mRNA isoforms that changed sig- nificantly with age in skeletal muscle were enriched for proteins involved in oxidative phosphorylation and adipogenesis. Our study establishes a detailed framework of the global transcriptome and mRNA isoforms that govern muscle damage and homeostasis with age. ✉ 1 National Institute on Aging–Intramural Research Program, National
    [Show full text]
  • University of Copenhagen
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Copenhagen University Research Information System TISSUES 2.0 an integrative web resource on mammalian tissue expression Palasca, Oana; Santos, Alberto; Stolte, Christian; Gorodkin, Jan; Jensen, Lars Juhl Published in: Database DOI: 10.1093/database/bay003 Publication date: 2018 Document version Publisher's PDF, also known as Version of record Citation for published version (APA): Palasca, O., Santos, A., Stolte, C., Gorodkin, J., & Jensen, L. J. (2018). TISSUES 2.0: an integrative web resource on mammalian tissue expression. Database, 2018(1), 1-12. https://doi.org/10.1093/database/bay003 Download date: 09. apr.. 2020 Database, 2018, 1–12 doi: 10.1093/database/bay003 Database update Downloaded from https://academic.oup.com/database/article-abstract/2018/1/bay003/4851151 by Det Kongelige Bibliotek user on 28 November 2018 Database update TISSUES 2.0: an integrative web resource on mammalian tissue expression Oana Palasca1,2,3,†, Alberto Santos1,†, Christian Stolte4, Jan Gorodkin2,3,* and Lars Juhl Jensen1,2,* 1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 2Center for non-coding RNA in Technology and Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 3Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark and 4New York Genome Center, New York, NY, USA *Corresponding author: Tel: þ45 35325025; Email: [email protected] Correspondence may also be addressed to Jan Gorodkin. Email: [email protected] Citation details: Palasca,O., Santos,A., Stolte,C.
    [Show full text]