DSIF and NELF Interact with Integrator to Specify the Correct Post-Transcriptional Fate of Snrna Genes
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation. -
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. -
Current Perspectives on the Role of TRAMP in Nuclear RNA Surveillance and Quality Control
Research and Reports in Biochemistry Dovepress open access to scientific and medical research Open Access Full Text Article REVIEW Current perspectives on the role of TRAMP in nuclear RNA surveillance and quality control Kewu Pan Abstract: The TRAMP complex assists the nuclear exosome to degrade a broad range of Zhe Huang ribonucleic acid (RNA) substrates by increasing both exoribonucleolytic activity and substrate Jimmy Tsz Hang Lee specificity. However, how the interactions between the TRAMP subunits and the components Chi-Ming Wong of the nuclear exosome regulate their functions in RNA degradation and substrate specificity remain unclear. This review aims to provide a summary of the recent findings on the role of State Key Laboratory of Pharmaceutical Biotechnology, the TRAMP complex in nuclear RNA degradation. The new insights from recent structural Department of Medicine, Shenzhen biological studies are discussed. Institute of Research and Innovation, Keywords: TRAMP, nuclear exosome, NEXT, RNA surveillance the University of Hong Kong, Pokfulam, Hong Kong Introduction TRAMP complex is one of the most well-characterized nuclear exosome cofactors, which enhances the activity and substrate specificity of the exosome.1 In Saccharomyces cerevisiae, TRAMP complex is a heterotrimeric complex consisting of a poly(A) poly- merase (either Trf4p or Trf5p); a zinc-knuckle ribonucleic acid (RNA)-binding protein (either Air1p or Air2p); and an RNA helicase Mtr4p.2,3 The main function of TRAMP is to assist the nuclear exosome to degrade a large -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
PDF Output of CLIC (Clustering by Inferred Co-Expression)
PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 14 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 14. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Cpsf3l Polr2b Ints3 Ints7 Ints1 Ints4 Ints9 Ints2 -
The Differential Interaction of Snrnps with Pre-Mrna Reveals Splicing Kinetics in Living Cells
Published October 4, 2010 This article has original data in the JCB Data Viewer JCB: Article http://jcb-dataviewer.rupress.org/jcb/browse/3011 The differential interaction of snRNPs with pre-mRNA reveals splicing kinetics in living cells Martina Huranová,1 Ivan Ivani,1 Aleš Benda,2 Ina Poser,3 Yehuda Brody,4,5 Martin Hof,2 Yaron Shav-Tal,4,5 Karla M. Neugebauer,3 and David StanČk1 1Institute of Molecular Genetics and 2J. Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, 142 20 Prague, Czech Republic 3Max Planck Institute for Molecular Cell Biology and Genetics, 01307 Dresden, Germany 4The Mina and Everard Goodman Faculty of Life Sciences and 5Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 52900, Israel recursor messenger RNA (pre-mRNA) splicing is Core components of the spliceosome, U2 and U5 snRNPs, catalyzed by the spliceosome, a large ribonucleo- associated with pre-mRNA for 15–30 s, indicating that protein (RNP) complex composed of five small nuclear splicing is accomplished within this time period. Additionally, P Downloaded from RNP particles (snRNPs) and additional proteins. Using live binding of U1 and U4/U6 snRNPs with pre-mRNA oc- cell imaging of GFP-tagged snRNP components expressed curred within seconds, indicating that the interaction of at endogenous levels, we examined how the spliceosome individual snRNPs with pre-mRNA is distinct. These results assembles in vivo. A comprehensive analysis of snRNP are consistent with the predictions of the step-wise model dynamics in the cell nucleus enabled us to determine of spliceosome assembly and provide an estimate on the snRNP diffusion throughout the nucleoplasm as well as rate of splicing in human cells. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Novel and Highly Recurrent Chromosomal Alterations in Se´Zary Syndrome
Research Article Novel and Highly Recurrent Chromosomal Alterations in Se´zary Syndrome Maarten H. Vermeer,1 Remco van Doorn,1 Remco Dijkman,1 Xin Mao,3 Sean Whittaker,3 Pieter C. van Voorst Vader,4 Marie-Jeanne P. Gerritsen,5 Marie-Louise Geerts,6 Sylke Gellrich,7 Ola So¨derberg,8 Karl-Johan Leuchowius,8 Ulf Landegren,8 Jacoba J. Out-Luiting,1 Jeroen Knijnenburg,2 Marije IJszenga,2 Karoly Szuhai,2 Rein Willemze,1 and Cornelis P. Tensen1 Departments of 1Dermatology and 2Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands; 3Department of Dermatology, St Thomas’ Hospital, King’s College, London, United Kingdom; 4Department of Dermatology, University Medical Center Groningen, Groningen, the Netherlands; 5Department of Dermatology, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands; 6Department of Dermatology, Gent University Hospital, Gent, Belgium; 7Department of Dermatology, Charite, Berlin, Germany; and 8Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, Uppsala, Sweden Abstract Introduction This study was designed to identify highly recurrent genetic Se´zary syndrome (Sz) is an aggressive type of cutaneous T-cell alterations typical of Se´zary syndrome (Sz), an aggressive lymphoma/leukemia of skin-homing, CD4+ memory T cells and is cutaneous T-cell lymphoma/leukemia, possibly revealing characterized by erythroderma, generalized lymphadenopathy, and pathogenetic mechanisms and novel therapeutic targets. the presence of neoplastic T cells (Se´zary cells) in the skin, lymph High-resolution array-based comparative genomic hybridiza- nodes, and peripheral blood (1). Sz has a poor prognosis, with a tion was done on malignant T cells from 20 patients. disease-specific 5-year survival of f24% (1). -
POLYADENYLATION REGULATION of U1A Mrna: CHARACTERIZING
STUDIES OF POLYADENYLATION REGULATION OF U1A mRNA BY AN RNP COMPLEX CONTAINING U1A AND U1 snRNP By ROSE MARIE CARATOZZOLO A dissertation submitted to the Graduate School – New Brunswick Rutgers, The State University of New Jersey and The Graduate School of Biomedical Sciences University of Medicine and Dentistry of New Jersey In partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate Program in Biochemistry Written under the direction of Samuel I. Gunderson, Ph.D., And approved by _____________________________ _____________________________ _____________________________ _____________________________ New Brunswick, New Jersey January, 2011 ABSTRACT OF THE DISSERTATION STUDIES OF POLYADENYLATION REGULATION OF U1A mRNA BY AN RNP COMPLEX CONTAINING U1A AND U1 snRNP By Rose Marie Caratozzolo Dissertation Director: Samuel I. Gunderson, Ph.D. The 3’-end processing of nearly all eukaryotic pre-mRNAs comprises multiple steps which culminate in the addition of a poly(A) tail, which is essential for mRNA stability, translation, and export. Consequently, polyadenylation regulation is an important component of gene expression. One way to regulate polyadenylation is to inhibit the activity of a single poly(A) site, as exemplified by the U1A protein that negatively autoregulates itself by binding to a Polyadenylation Inhibitory Element (PIE) site within the 3’ UTR of its own pre-mRNA. U1 snRNP, which is primarily involved in splice site recognition, inhibits poly(A) site activity in papillomaviruses by binding to 5’ splice site-like sequences, which have recently been named “U1-sites”. Here, a recently identified U1-site in the human U1A 3'UTR is examined and shown to synergize with the adjacent PIE site to inhibit polyadenylation. -
PHF22 (INTS12) (NM 020395) Human Over-Expression Lysate Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for LC412498 PHF22 (INTS12) (NM_020395) Human Over-expression Lysate Product data: Product Type: Over-expression Lysates Description: INTS12 HEK293T cell transient overexpression lysate (as WB positive control) Species: Human Expression Host: HEK293T Expression cDNA Clone TrueORF Clone RC204838 or AA Sequence: Tag: C-Myc/DDK Detection Antibodies: Clone OTI4C5, Anti-DDK (FLAG) monoclonal antibody (TA50011-100) Accession Number: NM_020395, NP_065128 Synonyms: INT12; PHF22; SBBI22 Predicted MW: 48.8 kDa Components: 1 vial of 20 ug lyophilized gene specific transient over-expression cell lysate Storage: The lysate can be shipped at ambient temperature. Upon receiving, store the sample at - 20°C. Lysate samples can be reconstituted with SDS Sample Buffer. Avoid repeated freeze- thaw cycles after reconstitution. Lysate samples are stable for 12 months from date of receipt when stored at -20°C. Preparation: HEK293T cells in 10-cm dishes were transiently transfected withM egaTran Transfection Reagent (TT200002) and 5ug TrueORF cDNA plasmid. Transfected cells were cultured for 48hrs before collection. The cells were lysed in modified RIPA buffer (25mM Tris-HCl pH7.6, 150mM NaCl, 1% NP-40, 1mM EDTA, 1xProteinase inhibitor cocktail mix (Sigma), 1mM PMSF and 1mM Na3VO4), and then centrifuged to clarify the lysate. Protein concentration was measured by BCA kit (Thermo Scientific Inc.). To facilitate transportation and protein, the products are supplied as lyophilized proteins. RefSeq: NP_065128 Locus ID: 57117 Cytogenetics: 4q24 Protein Families: Druggable Genome, Transcription Factors This product is to be used for laboratory only. -
Bioinformatics Analysis for the Identification of Differentially Expressed Genes and Related Signaling Pathways in H
Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells Dingyu Chen*, Chao Li, Yan Zhao, Jianjiang Zhou, Qinrong Wang and Yuan Xie* Key Laboratory of Endemic and Ethnic Diseases , Ministry of Education, Guizhou Medical University, Guiyang, China * These authors contributed equally to this work. ABSTRACT Aim. Helicobacter pylori cytotoxin-associated protein A (CagA) is an important vir- ulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA- induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods. AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, | logFC |> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Submitted 20 August 2020 Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the Accepted 11 March 2021 key genes derived from the PPI network. Further analysis of the key gene expressions Published 15 April 2021 in gastric cancer and normal tissues were performed based on The Cancer Genome Corresponding author Atlas (TCGA) database and RT-qPCR verification. -
Pseudogene INTS6P1 Regulates Its Cognate Gene INTS6 Through Competitive Binding of Mir-17-5P in Hepatocellular Carcinoma
www.impactjournals.com/oncotarget/ Oncotarget, Vol. 6, No.8 Pseudogene INTS6P1 regulates its cognate gene INTS6 through competitive binding of miR-17-5p in hepatocellular carcinoma Haoran Peng1,3, Masaharu Ishida1, Ling Li1, Atsushi Saito2, Atsushi Kamiya2, James P. Hamilton1, Rongdang Fu3, Alexandru V. Olaru1, Fangmei An4, Irinel Popescu5, Razvan Iacob5, Simona Dima5, Sorin T. Alexandrescu5, Razvan Grigorie5, Anca Nastase5, Ioana Berindan-Neagoe6,7,8, Ciprian Tomuleasa8,9, Florin Graur10,11, Florin Zaharia10,11, Michael S. Torbenson12, Esteban Mezey1, Minqiang Lu3 and Florin M. Selaru1,13 1 Division of Gastroenterology and Hepatology, Department of Medicine, The Johns Hopkins Hospital, Baltimore, Maryland, USA 2 Department of Psychiatry and Behavioral Sciences, The Johns Hopkins Hospital, Baltimore, Maryland, USA 3 Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, P.R. China 4 Department of Gastroenterology, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, P.R. China 5 Dan Setlacec Center of General Surgery and Liver Transplantation, Fundeni Clinical Institute, Bucharest, Romania 6 Department of Immunology, The Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania 7 Department of Functional Genomics, The Oncology Institute Ion Chiricuta, Cluj Napoca, Romania 8 The Research Center for Functional Genomics, Biomedicine and Translational Medicine, The Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania 9 Department of Hematology, The Oncology Institute Ion Chiricuta, Cluj Napoca, Romania 10 Department of Surgery, The Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania 11 Department of Surgery, Regional Institute of Gastroenterology and Hepatology “Octavian Fodor”, Cluj Napoca, Romania 12 Department of Pathology, The Johns Hopkins Hospital, Baltimore, Maryland, USA 13 The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins Hospital, Baltimore, Maryland, USA Correspondence to: Florin M.