Trypanosoma Brucei Ribonuclease H2A Is an Essential Enzyme That Resolves R-Loops Associated with Transcription Initiation and Antigenic Variation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Peripheral Neuropathy in Complex Inherited Diseases: an Approach To
PERIPHERAL NEUROPATHY IN COMPLEX INHERITED DISEASES: AN APPROACH TO DIAGNOSIS Rossor AM1*, Carr AS1*, Devine H1, Chandrashekar H2, Pelayo-Negro AL1, Pareyson D3, Shy ME4, Scherer SS5, Reilly MM1. 1. MRC Centre for Neuromuscular Diseases, UCL Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK. 2. Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK. 3. Unit of Neurological Rare Diseases of Adulthood, Carlo Besta Neurological Institute IRCCS Foundation, Milan, Italy. 4. Department of Neurology, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA 5. Department of Neurology, University of Pennsylvania, Philadelphia, PA 19014, USA. * These authors contributed equally to this work Corresponding author: Mary M Reilly Address: MRC Centre for Neuromuscular Diseases, 8-11 Queen Square, London, WC1N 3BG, UK. Email: [email protected] Telephone: 0044 (0) 203 456 7890 Word count: 4825 ABSTRACT Peripheral neuropathy is a common finding in patients with complex inherited neurological diseases and may be subclinical or a major component of the phenotype. This review aims to provide a clinical approach to the diagnosis of this complex group of patients by addressing key questions including the predominant neurological syndrome associated with the neuropathy e.g. spasticity, the type of neuropathy, and the other neurological and non- neurological features of the syndrome. Priority is given to the diagnosis of treatable conditions. Using this approach, we associated neuropathy with one of three major syndromic categories - 1) ataxia, 2) spasticity, and 3) global neurodevelopmental impairment. Syndromes that do not fall easily into one of these three categories can be grouped according to the predominant system involved in addition to the neuropathy e.g. -
Practice Parameter for the Diagnosis and Management of Primary Immunodeficiency
Practice parameter Practice parameter for the diagnosis and management of primary immunodeficiency Francisco A. Bonilla, MD, PhD, David A. Khan, MD, Zuhair K. Ballas, MD, Javier Chinen, MD, PhD, Michael M. Frank, MD, Joyce T. Hsu, MD, Michael Keller, MD, Lisa J. Kobrynski, MD, Hirsh D. Komarow, MD, Bruce Mazer, MD, Robert P. Nelson, Jr, MD, Jordan S. Orange, MD, PhD, John M. Routes, MD, William T. Shearer, MD, PhD, Ricardo U. Sorensen, MD, James W. Verbsky, MD, PhD, David I. Bernstein, MD, Joann Blessing-Moore, MD, David Lang, MD, Richard A. Nicklas, MD, John Oppenheimer, MD, Jay M. Portnoy, MD, Christopher R. Randolph, MD, Diane Schuller, MD, Sheldon L. Spector, MD, Stephen Tilles, MD, Dana Wallace, MD Chief Editor: Francisco A. Bonilla, MD, PhD Co-Editor: David A. Khan, MD Members of the Joint Task Force on Practice Parameters: David I. Bernstein, MD, Joann Blessing-Moore, MD, David Khan, MD, David Lang, MD, Richard A. Nicklas, MD, John Oppenheimer, MD, Jay M. Portnoy, MD, Christopher R. Randolph, MD, Diane Schuller, MD, Sheldon L. Spector, MD, Stephen Tilles, MD, Dana Wallace, MD Primary Immunodeficiency Workgroup: Chairman: Francisco A. Bonilla, MD, PhD Members: Zuhair K. Ballas, MD, Javier Chinen, MD, PhD, Michael M. Frank, MD, Joyce T. Hsu, MD, Michael Keller, MD, Lisa J. Kobrynski, MD, Hirsh D. Komarow, MD, Bruce Mazer, MD, Robert P. Nelson, Jr, MD, Jordan S. Orange, MD, PhD, John M. Routes, MD, William T. Shearer, MD, PhD, Ricardo U. Sorensen, MD, James W. Verbsky, MD, PhD GlaxoSmithKline, Merck, and Aerocrine; has received payment for lectures from Genentech/ These parameters were developed by the Joint Task Force on Practice Parameters, representing Novartis, GlaxoSmithKline, and Merck; and has received research support from Genentech/ the American Academy of Allergy, Asthma & Immunology; the American College of Novartis and Merck. -
Lineage-Specific Programming Target Genes Defines Potential for Th1 Temporal Induction Pattern of STAT4
Downloaded from http://www.jimmunol.org/ by guest on October 1, 2021 is online at: average * The Journal of Immunology published online 26 August 2009 from submission to initial decision 4 weeks from acceptance to publication J Immunol http://www.jimmunol.org/content/early/2009/08/26/jimmuno l.0901411 Temporal Induction Pattern of STAT4 Target Genes Defines Potential for Th1 Lineage-Specific Programming Seth R. Good, Vivian T. Thieu, Anubhav N. Mathur, Qing Yu, Gretta L. Stritesky, Norman Yeh, John T. O'Malley, Narayanan B. Perumal and Mark H. Kaplan Submit online. Every submission reviewed by practicing scientists ? is published twice each month by http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://www.jimmunol.org/content/suppl/2009/08/26/jimmunol.090141 1.DC1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* • Why • • Material Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2009 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of October 1, 2021. Published August 26, 2009, doi:10.4049/jimmunol.0901411 The Journal of Immunology Temporal Induction Pattern of STAT4 Target Genes Defines Potential for Th1 Lineage-Specific Programming1 Seth R. Good,2* Vivian T. Thieu,2† Anubhav N. Mathur,† Qing Yu,† Gretta L. -
Supplementary Methods
Supplementary methods Human lung tissues and tissue microarray (TMA) All human tissues were obtained from the Lung Cancer Specialized Program of Research Excellence (SPORE) Tissue Bank at the M.D. Anderson Cancer Center (Houston, TX). A collection of 26 lung adenocarcinomas and 24 non-tumoral paired tissues were snap-frozen and preserved in liquid nitrogen for total RNA extraction. For each tissue sample, the percentage of malignant tissue was calculated and the cellular composition of specimens was determined by histological examination (I.I.W.) following Hematoxylin-Eosin (H&E) staining. All malignant samples retained contained more than 50% tumor cells. Specimens resected from NSCLC stages I-IV patients who had no prior chemotherapy or radiotherapy were used for TMA analysis by immunohistochemistry. Patients who had smoked at least 100 cigarettes in their lifetime were defined as smokers. Samples were fixed in formalin, embedded in paraffin, stained with H&E, and reviewed by an experienced pathologist (I.I.W.). The 413 tissue specimens collected from 283 patients included 62 normal bronchial epithelia, 61 bronchial hyperplasias (Hyp), 15 squamous metaplasias (SqM), 9 squamous dysplasias (Dys), 26 carcinomas in situ (CIS), as well as 98 squamous cell carcinomas (SCC) and 141 adenocarcinomas. Normal bronchial epithelia, hyperplasia, squamous metaplasia, dysplasia, CIS, and SCC were considered to represent different steps in the development of SCCs. All tumors and lesions were classified according to the World Health Organization (WHO) 2004 criteria. The TMAs were prepared with a manual tissue arrayer (Advanced Tissue Arrayer ATA100, Chemicon International, Temecula, CA) using 1-mm-diameter cores in triplicate for tumors and 1.5 to 2-mm cores for normal epithelial and premalignant lesions. -
Ribonuclease H1-Dependent Hepatotoxicity Caused by Locked
www.nature.com/scientificreports OPEN Ribonuclease H1-dependent hepatotoxicity caused by locked nucleic acid-modified gapmer Received: 29 March 2016 Accepted: 30 June 2016 antisense oligonucleotides Published: 27 July 2016 Takeshi Kasuya1, Shin-ichiro Hori1, Ayahisa Watanabe2, Mado Nakajima1, Yoshinari Gahara1, Masatomo Rokushima1, Toru Yanagimoto1,* & Akira Kugimiya1,* Gapmer antisense oligonucleotides cleave target RNA effectivelyin vivo, and is considered as promising therapeutics. Especially, gapmers modified with locked nucleic acid (LNA) shows potent knockdown activity; however, they also cause hepatotoxic side effects. For developing safe and effective gapmer drugs, a deeper understanding of the mechanisms of hepatotoxicity is required. Here, we investigated the cause of hepatotoxicity derived from LNA-modified gapmers. Chemical modification of gapmer’s gap region completely suppressed both knockdown activity and hepatotoxicity, indicating that the root cause of hepatotoxicity is related to intracellular gapmer activity. Gene silencing of hepatic ribonuclease H1 (RNaseH1), which catalyses gapmer-mediated RNA knockdown, strongly supressed hepatotoxic effects. Small interfering RNA (siRNA)-mediated knockdown of a target mRNA did not result in any hepatotoxic effects, while the gapmer targeting the same position on mRNA as does the siRNA showed acute toxicity. Microarray analysis revealed that several pre-mRNAs containing a sequence similar to the gapmer target were also knocked down. These results suggest that hepatotoxicity of LNA gapmer is caused by RNAseH1 activity, presumably because of off-target cleavage of RNAs inside nuclei. The 5′ and 3′ end-modified antisense oligonucleotide (ASO), gapmer, is promising therapeutic agent which can modulate target RNA expression1,2. Various types of chemical modification of ASOs have been examined to enhance nuclease resistance, to improve stability of ASO–RNA hybrids, and to increase RNA-manipulating effects. -
Resveratrol Inhibits Cell Cycle Progression by Targeting Aurora Kinase a and Polo-Like Kinase 1 in Breast Cancer Cells
3696 ONCOLOGY REPORTS 35: 3696-3704, 2016 Resveratrol inhibits cell cycle progression by targeting Aurora kinase A and Polo-like kinase 1 in breast cancer cells RUBICELI MEDINA-AGUILAR1, LAURENCE A. Marchat2, ELENA ARECHAGA OCAMPO3, Patricio GARIGLIO1, JAIME GARCÍA MENA1, NICOLÁS VILLEGAS SEPÚlveda4, MACARIO MartÍNEZ CASTILLO4 and CÉSAR LÓPEZ-CAMARILLO5 1Department of Genetics and Molecular Biology, CINVESTAV-IPN, Mexico D.F.; 2Molecular Biomedicine Program and Biotechnology Network, National School of Medicine and Homeopathy, National Polytechnic Institute, Mexico D.F.; 3Natural Sciences Department, Metropolitan Autonomous University, Mexico D.F.; 4Department of Molecular Biomedicine, CINVESTAV-IPN, Mexico D.F.; 5Oncogenomics and Cancer Proteomics Laboratory, Universidad Autónoma de la Ciudad de México, Mexico D.F., Mexico Received December 4, 2015; Accepted January 8, 2016 DOI: 10.3892/or.2016.4728 Abstract. The Aurora protein kinase (AURKA) and the MDA-MB-231 and MCF-7 cells. By western blot assays, we Polo-like kinase-1 (PLK1) activate the cell cycle, and they confirmed that resveratrol suppressed AURKA, CCND1 and are considered promising druggable targets in cancer CCNB1 at 24 and 48 h. In summary, we showed for the first time therapy. However, resistance to chemotherapy and to specific that resveratrol regulates cell cycle progression by targeting small-molecule inhibitors is common in cancer patients; thus AURKA and PLK1. Our findings highlight the potential use of alternative therapeutic approaches are needed to overcome resveratrol as an adjuvant therapy for breast cancer. clinical resistance. Here, we showed that the dietary compound resveratrol suppressed the cell cycle by targeting AURKA Introduction and PLK1 kinases. First, we identified genes modulated by resveratrol using a genome-wide analysis of gene expression Cancer development results from the interaction between in MDA-MB-231 breast cancer cells. -
Leukodystrophies by Raphael Schiffmann MD (Dr
Leukodystrophies By Raphael Schiffmann MD (Dr. Schiffmann, Director of the Institute of Metabolic Disease at Baylor Research Institute, received research grants from Amicus Therapeutics, Protalix Biotherapeutics, and Shire.) Originally released January 17, 2013; last updated November 25, 2016; expires November 25, 2019 Introduction Overview Leukodystrophies are a heterogeneous group of genetic disorders affecting the white matter of the central nervous system and sometimes with peripheral nervous system involvement. There are over 30 different leukodystrophies, with an overall population incidence of 1 in 7663 live births. They are now most commonly grouped based on the initial pattern of central nervous system white matter abnormalities on neuroimaging. All leukodystrophies have MRI hyperintense white matter on T2-weighted images, whereas T1 signal may be variable. Mildly hypo-, iso-, or hyperintense T1 signal relative to the cortex suggests a hypomyelinating pattern. A significantly hypointense T1 signal is more often associated with demyelination or other pathologies. Recognition of the abnormal MRI pattern in leukodystrophies greatly facilitates its diagnosis. Early diagnosis is important for genetic counseling and appropriate therapy where available. Key points • Leukodystrophies are classically defined as progressive genetic disorders that predominantly affect the white matter of the brain. • The pattern of abnormalities on brain MRI, and sometimes brain CT, is the most useful diagnostic tool. • Radial diffusivity on brain diffusion weighted imaging correlates with motor handicap. • X-linked adrenoleukodystrophy is the most common leukodystrophy and has effective therapy if applied early in the disease course. • Lentiviral hemopoietic stem-cell gene therapy in early-onset metachromatic leukodystrophy shows promise. Historical note and terminology The first leukodystrophies were identified early last century. -
Gene Symbol Gene Name No Stimulation (RPKM) R1881 (RPKM
No R1881 + R1881 + R1881 Gene Symbol Gene Name stimulation Enzalutamide GNE-049 (RPKM) (RPKM) (RPKM) (RPKM) cholinergic receptor, CHRNA2 nicotinic, alpha 2 0.136 2.08 0.0913 4.35 (neuronal) kallikrein-related KLK2 2.9 43.9 1.46 26.4 peptidase 2 kallikrein-related KLK3 36 292 19.2 0.879 peptidase 3 ST6 (alpha-N-acetyl- neuraminyl-2,3-beta- galactosyl-1,3)-N- ST6GALNAC1 0.863 6.3 0.595 3.19 acetylgalactosaminide alpha-2,6- sialyltransferase 1 KLKP1 kallikrein pseudogene 1 0.791 4.28 0.535 5.5 kallikrein-related KLK4 48.3 225 29.9 4.18 peptidase 4 denticleless E3 ubiquitin DTL protein ligase homolog 1.2 4.63 1.01 18.4 (Drosophila) ZNF367 zinc finger protein 367 0.651 2.5 0.548 0.399 ribonucleotide reductase RRM2 2.33 8.57 1.99 8.05 M2 family with sequence FAM111B 4.48 16.1 3.32 75.6 similarity 111, member B solute carrier family 45, SLC45A3 13.1 47 9.11 17.6 member 3 solute carrier family 15 SLC15A2 (oligopeptide 0.665 2.37 0.576 20 transporter), member 2 potassium intermediate/small KCNN2 conductance calcium- 4.88 17.2 4.02 0.0617 activated channel, subfamily N, member 2 minichromosome MCM10 maintenance complex 0.824 2.76 0.572 2.02 component 10 CDC45 cell division cycle 45 0.967 3.23 0.919 53 CDC6 cell division cycle 6 2.72 8.73 2.43 2.26 C19orf48 chromosome 19 open 31.2 100 24.9 14.2 reading frame 48 LOC10192707 uncharacterized 0.76 2.32 0.537 0.0958 8 LOC101927078 centrosomal protein CEP55 0.822 2.44 0.679 4.26 55kDa origin recognition ORC6 1.96 5.76 1.94 7.87 complex, subunit 6 EAF2 ELL associated factor 2 3.07 8.92 2.56 12.2 prostate transmembrane PMEPA1 protein, androgen 8.63 24.9 6.86 2.74 induced 1 DNA damage-induced DDIAS 0.762 2.17 0.742 0.338 apoptosis suppressor transmembrane TMPRSS2 30.4 85.1 19.6 32.6 protease, serine 2 anti-silencing function 1B ASF1B 3.48 9.64 2.57 12.5 histone chaperone RAD54L RAD54-like (S. -
Ncounter® Human Autoimmune Profiling Panel
nCounter® Human AutoImmune Profiling Panel - Gene and Probe Details Official Symbol Accession Alias / Previous Symbol Official Full Name Other targets or Isoform Information ACE NM_000789.2 DCP1;angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 angiotensin I converting enzyme ACIN1 NM_001164815.1 ACINUS;apoptotic chromatin condensation inducer in the nucleus apoptotic chromatin condensation inducer 1 ACP5 NM_001611.3 acid phosphatase 5, tartrate resistant CTRN2;ARP1 (actin-related protein 1, yeast) homolog B (centractin beta),ARP1 actin-related ACTR1B NM_005735.3 protein 1 homolog B, centractin beta ARP1 actin related protein 1 homolog B ADAM17 NM_003183.4 TACE;tumor necrosis factor, alpha, converting enzyme ADAM metallopeptidase domain 17 ADAR NM_001111.3 IFI4,G1P1;interferon-induced protein 4 adenosine deaminase, RNA specific ADORA2A NM_000675.3 ADORA2 adenosine A2a receptor AGER NM_001136.3 advanced glycosylation end-product specific receptor AGT NM_000029.3 SERPINA8;serpin peptidase inhibitor, clade A, member 8 angiotensinogen AHR NM_001621.3 aryl hydrocarbon receptor AICDA NM_020661.2 activation-induced cytidine deaminase activation induced cytidine deaminase AIM2 NM_004833.1 absent in melanoma 2 APECED;autoimmune regulator (autoimmune polyendocrinopathy candidiasis ectodermal AIRE NM_000383.2 dystrophy) autoimmune regulator AKT1 NM_001014432.1 v-akt murine thymoma viral oncogene homolog 1 AKT serine/threonine kinase 1 AKT2 NM_001626.4 v-akt murine thymoma viral oncogene homolog 2 AKT serine/threonine kinase 2 AKT3 NM_005465.4 -
Transdifferentiation of Human Mesenchymal Stem Cells
Transdifferentiation of Human Mesenchymal Stem Cells Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von Tatjana Schilling aus San Miguel de Tucuman, Argentinien Würzburg, 2007 Eingereicht am: Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Martin J. Müller Gutachter: PD Dr. Norbert Schütze Gutachter: Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am: Hiermit erkläre ich ehrenwörtlich, dass ich die vorliegende Dissertation selbstständig angefertigt und keine anderen als die von mir angegebenen Hilfsmittel und Quellen verwendet habe. Des Weiteren erkläre ich, dass diese Arbeit weder in gleicher noch in ähnlicher Form in einem Prüfungsverfahren vorgelegen hat und ich noch keinen Promotionsversuch unternommen habe. Gerbrunn, 4. Mai 2007 Tatjana Schilling Table of contents i Table of contents 1 Summary ........................................................................................................................ 1 1.1 Summary.................................................................................................................... 1 1.2 Zusammenfassung..................................................................................................... 2 2 Introduction.................................................................................................................... 4 2.1 Osteoporosis and the fatty degeneration of the bone marrow..................................... 4 2.2 Adipose and bone -
Downregulation of Carnitine Acyl-Carnitine Translocase by Mirnas
Page 1 of 288 Diabetes 1 Downregulation of Carnitine acyl-carnitine translocase by miRNAs 132 and 212 amplifies glucose-stimulated insulin secretion Mufaddal S. Soni1, Mary E. Rabaglia1, Sushant Bhatnagar1, Jin Shang2, Olga Ilkayeva3, Randall Mynatt4, Yun-Ping Zhou2, Eric E. Schadt6, Nancy A.Thornberry2, Deborah M. Muoio5, Mark P. Keller1 and Alan D. Attie1 From the 1Department of Biochemistry, University of Wisconsin, Madison, Wisconsin; 2Department of Metabolic Disorders-Diabetes, Merck Research Laboratories, Rahway, New Jersey; 3Sarah W. Stedman Nutrition and Metabolism Center, Duke Institute of Molecular Physiology, 5Departments of Medicine and Pharmacology and Cancer Biology, Durham, North Carolina. 4Pennington Biomedical Research Center, Louisiana State University system, Baton Rouge, Louisiana; 6Institute for Genomics and Multiscale Biology, Mount Sinai School of Medicine, New York, New York. Corresponding author Alan D. Attie, 543A Biochemistry Addition, 433 Babcock Drive, Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, (608) 262-1372 (Ph), (608) 263-9608 (fax), [email protected]. Running Title: Fatty acyl-carnitines enhance insulin secretion Abstract word count: 163 Main text Word count: 3960 Number of tables: 0 Number of figures: 5 Diabetes Publish Ahead of Print, published online June 26, 2014 Diabetes Page 2 of 288 2 ABSTRACT We previously demonstrated that micro-RNAs 132 and 212 are differentially upregulated in response to obesity in two mouse strains that differ in their susceptibility to obesity-induced diabetes. Here we show the overexpression of micro-RNAs 132 and 212 enhances insulin secretion (IS) in response to glucose and other secretagogues including non-fuel stimuli. We determined that carnitine acyl-carnitine translocase (CACT, Slc25a20) is a direct target of these miRNAs.