Mouse Utp15 Knockout Project (CRISPR/Cas9)
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. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Mouse Utp15 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Utp15 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Utp15 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Utp15 gene (NCBI Reference Sequence: NM_178918 ; Ensembl: ENSMUSG00000041747 ) is located on Mouse chromosome 13. 13 exons are identified, with the ATG start codon in exon 2 and the TAA stop codon in exon 13 (Transcript: ENSMUST00000040972). Exon 3~4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Utp15 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-267C22 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 5.74% of the coding region. The knockout of Exon 3~4 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 1332 bp, and the size of intron 4 for 3'-loxP site insertion: 1090 bp. The size of effective cKO region: ~862 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 3 4 5 6 13 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Utp15 Homology arm cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
The Genetic Basis for Individual Differences in Mrna Splicing and APOBEC1 Editing Activity in Murine Macrophages
The genetic basis for individual differences in mRNA splicing and APOBEC1 editing activity in murine macrophages The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hassan, M. A., V. Butty, K. D. C. Jensen, and J. P. J. Saeij. “The Genetic Basis for Individual Differences in mRNA Splicing and APOBEC1 Editing Activity in Murine Macrophages.” Genome Research 24, no. 3 (March 1, 2014): 377–389. As Published http://dx.doi.org/10.1101/gr.166033.113 Publisher Cold Spring Harbor Laboratory Press Version Final published version Citable link http://hdl.handle.net/1721.1/89091 Terms of Use Creative Commons Attribution-Noncommerical Detailed Terms http://creativecommons.org/licenses/by-nc/3.0/ Downloaded from genome.cshlp.org on August 27, 2014 - Published by Cold Spring Harbor Laboratory Press The genetic basis for individual differences in mRNA splicing and APOBEC1 editing activity in murine macrophages Musa A. Hassan, Vincent Butty, Kirk D.C. Jensen, et al. Genome Res. 2014 24: 377-389 originally published online November 18, 2013 Access the most recent version at doi:10.1101/gr.166033.113 Supplemental http://genome.cshlp.org/content/suppl/2014/01/07/gr.166033.113.DC1.html Material References This article cites 95 articles, 32 of which can be accessed free at: http://genome.cshlp.org/content/24/3/377.full.html#ref-list-1 Creative This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the Commons first six months after the full-issue publication date (see License http://genome.cshlp.org/site/misc/terms.xhtml). -
Utpa and Utpb Chaperone Nascent Pre-Ribosomal RNA and U3 Snorna to Initiate Eukaryotic Ribosome Assembly
ARTICLE Received 6 Apr 2016 | Accepted 27 May 2016 | Published 29 Jun 2016 DOI: 10.1038/ncomms12090 OPEN UtpA and UtpB chaperone nascent pre-ribosomal RNA and U3 snoRNA to initiate eukaryotic ribosome assembly Mirjam Hunziker1,*, Jonas Barandun1,*, Elisabeth Petfalski2, Dongyan Tan3, Cle´mentine Delan-Forino2, Kelly R. Molloy4, Kelly H. Kim5, Hywel Dunn-Davies2, Yi Shi4, Malik Chaker-Margot1,6, Brian T. Chait4, Thomas Walz5, David Tollervey2 & Sebastian Klinge1 Early eukaryotic ribosome biogenesis involves large multi-protein complexes, which co-transcriptionally associate with pre-ribosomal RNA to form the small subunit processome. The precise mechanisms by which two of the largest multi-protein complexes—UtpA and UtpB—interact with nascent pre-ribosomal RNA are poorly understood. Here, we combined biochemical and structural biology approaches with ensembles of RNA–protein cross-linking data to elucidate the essential functions of both complexes. We show that UtpA contains a large composite RNA-binding site and captures the 50 end of pre-ribosomal RNA. UtpB forms an extended structure that binds early pre-ribosomal intermediates in close proximity to architectural sites such as an RNA duplex formed by the 50 ETS and U3 snoRNA as well as the 30 boundary of the 18S rRNA. Both complexes therefore act as vital RNA chaperones to initiate eukaryotic ribosome assembly. 1 Laboratory of Protein and Nucleic Acid Chemistry, The Rockefeller University, New York, New York 10065, USA. 2 Wellcome Trust Centre for Cell Biology, University of Edinburgh, Michael Swann Building, Max Born Crescent, Edinburgh EH9 3BF, UK. 3 Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA. -
The Complete Structure of the Small-Subunit Processome
ARTICLES The complete structure of the small-subunit processome Jonas Barandun1,4 , Malik Chaker-Margot1,2,4 , Mirjam Hunziker1,4 , Kelly R Molloy3, Brian T Chait3 & Sebastian Klinge1 The small-subunit processome represents the earliest stable precursor of the eukaryotic small ribosomal subunit. Here we present the cryo-EM structure of the Saccharomyces cerevisiae small-subunit processome at an overall resolution of 3.8 Å, which provides an essentially complete near-atomic model of this assembly. In this nucleolar superstructure, 51 ribosome-assembly factors and two RNAs encapsulate the 18S rRNA precursor and 15 ribosomal proteins in a state that precedes pre-rRNA cleavage at site A1. Extended flexible proteins are employed to connect distant sites in this particle. Molecular mimicry and steric hindrance, as well as protein- and RNA-mediated RNA remodeling, are used in a concerted fashion to prevent the premature formation of the central pseudoknot and its surrounding elements within the small ribosomal subunit. Eukaryotic ribosome assembly is a highly dynamic process involving during which the four rRNA domains of the 18S rRNA (5′, central, in excess of 200 non-ribosomal proteins and RNAs. This process 3′ major and 3′ minor) are bound by a set of specific ribosome-assem- starts in the nucleolus where rRNAs for the small ribosomal subunit bly factors7,8. Biochemical studies indicated that these factors and the (18S rRNA) and the large ribosomal subunit (25S and 5.8S rRNA) 5′-ETS particle probably contribute to the independent maturation are initially transcribed as part of a large 35S pre-rRNA precursor of the domains of the SSU7,8. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
The Nuclear Poly(A) Polymerase and Exosome Cofactor Trf5 Is Recruited Cotranscriptionally to Nucleolar Surveillance
Downloaded from rnajournal.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press The nuclear poly(A) polymerase and Exosome cofactor Trf5 is recruited cotranscriptionally to nucleolar surveillance MAXIME WERY,1 SABINE RUIDANT,1 STE´PHANIE SCHILLEWAERT, NATHALIE LEPORE´, and DENIS L.J. LAFONTAINE Fonds de la Recherche Scientifique (FRS-FNRS), Acade´mie Wallonie-Bruxelles, Institut de Biologie et de Me´decine Mole´culaires, Universite´ Libre de Bruxelles, Charleroi-Gosselies, B-6041 Belgium ABSTRACT Terminal balls detected at the 59-end of nascent ribosomal transcripts act as pre-rRNA processing complexes and are detected in all eukaryotes examined, resulting in illustrious Christmas tree images. Terminal balls (also known as SSU-processomes) compaction reflects the various stages of cotranscriptional ribosome assembly. Here, we have followed SSU-processome compaction in vivo by use of a chromatin immunoprecipitation (Ch-IP) approach and shown, in agreement with electron microscopy analysis of Christmas trees, that it progressively condenses to come in close proximity to the 59-end of the 25S rRNA gene. The SSU-processome is comprised of independent autonomous building blocks that are loaded onto nascent pre-rRNAs and assemble into catalytically active pre-rRNA processing complexes in a stepwise and highly hierarchical process. Failure to assemble SSU-processome subcomplexes with proper kinetics triggers a nucleolar surveillance pathway that targets misassembled pre-rRNAs otherwise destined to mature into small subunit 18S rRNA for polyadenylation, preferentially by TRAMP5, and degradation by the 39 to 59 exoribonucleolytic activity of the Exosome. Trf5 colocalized with nascent pre-rRNPs, indicating that this nucleolar surveillance initiates cotranscriptionally. -
RNA Polymerase I Transcription and Pre-Rrna Processing Are Linked by Specific SSU Processome Components
Downloaded from genesdev.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press RNA polymerase I transcription and pre-rRNA processing are linked by specific SSU processome components Jennifer E.G. Gallagher,2 David A. Dunbar,3 Sander Granneman,1 Brianna M. Mitchell,3 Yvonne Osheim,4 Ann L. Beyer,4 and Susan J. Baserga1,2,3,5 1Department of Molecular Biophysics and Biochemistry, 2Department of Genetics, and 3Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06520-8024, USA; 4Department of Microbiology, University of Virginia, Charlottesville, Virginia 22904, USA Sequential events in macromolecular biosynthesis are often elegantly coordinated. The small ribosomal subunit (SSU) processome is a large ribonucleoprotein (RNP) required for processing of precursors to the small subunit RNA, the 18S, of the ribosome. We have found that a subcomplex of SSU processome proteins, the t-Utps, is also required for optimal rRNA transcription in vivo in the yeast Saccharomyces cerevisiae.The t-Utps are ribosomal chromatin (r-chromatin)-associated, and they exist in a complex in the absence of the U3 snoRNA. Transcription is required neither for the formation of the subcomplex nor for its r-chromatin association. The t-Utps are associated with the pre-18S rRNAs independent of the presence of the U3 snoRNA. This association may thus represent an early step in the formation of the SSU processome. Our results indicate that rRNA transcription and pre-rRNA processing are coordinated via specific components of the SSU processome. [Keywords: Ribosome; RNA; transcription; RNA processing; SSU processome] Received May 27, 2004; revised version accepted August 19, 2004. -
The Synthetic Genetic Interaction Spectrum of Essential Genes
LETTERS The synthetic genetic interaction spectrum of essential genes Armaity P Davierwala1, Jennifer Haynes2, Zhijian Li1,2, Rene´e L Brost1, Mark D Robinson1, Lisa Yu3, Sanie Mnaimneh1, Huiming Ding1, Hongwei Zhu1, Yiqun Chen1, Xin Cheng1, Grant W Brown3, Charles Boone1,2,4, Brenda J Andrews1,2,4 & Timothy R Hughes1,2,4 The nature of synthetic genetic interactions involving essential mapping these genetic interactions2,3. Essential gene function is also genes (those required for viability) has not been previously buffered by both nonessential and other essential genes because hypomorphic (partially functional) alleles of essential genes often http://www.nature.com/naturegenetics examined in a broad and unbiased manner. We crossed yeast strains carrying promoter-replacement alleles for more than have synthetic lethal interactions with deletion alleles of nonessential half of all essential yeast genes1 to a panel of 30 different genes and hypomorphic alleles of other essential genes2,3,7.Genetic mutants with defects in diverse cellular processes. The resulting interactions among essential genes have not been examined system- genetic network is biased toward interactions between atically and objectively because of the inherent difficulty in creating functionally related genes, enabling identification of a and working with hypomorphic alleles. previously uncharacterized essential gene (PGA1) required for specific functions of the endoplasmic reticulum. But there are also many interactions between genes with dissimilar functions, Query gene (crossed to array) Gene Ontology labels (array strains) suggesting that individual essential genes are required for buffering many cellular processes. The most notable feature of the essential synthetic genetic network is that it has an interaction density five times that of nonessential synthetic 2005 Nature Publishing Group Group 2005 Nature Publishing genetic networks2,3, indicating that most yeast genetic © -linked glycosylation -linked interactions involve at least one essential gene. -
Transcriptomic Changes Resulting from STK32B Overexpression Identifies Pathways Potentially Relevant to Essential Tremor
bioRxiv preprint doi: https://doi.org/10.1101/552901; this version posted May 10, 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. 1 Transcriptomic changes resulting from 2 STK$%B overexpression identifies pathways 3 potentially relevant to essential tremor 4 Calwing Liao.,0, Faezeh Sarayloo.,0, Veikko Vuokila0, Daniel Rochefort0, Fulya Akçimen.,0, 5 Simone Diamond0, Alexandre D. Laporte0, Dan Spiegelman0, Qin HeJ, Hélène Catoire0, Patrick A. 6 Dion0,O, Guy A. Rouleau.,0,O 7 8 .Department of Human GeneticS, McGill University, Montréal, Quebec, Canada 9 0Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada. 10 JDepartment of Biomedical ScienceS, Université de Montréal, Montréal, Quebec, Canada 11 ODepartment of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada 12 13 Corresponding Author: 14 Dr. Guy A. Rouleau, MD, PhD, FRCPC, OQ 15 Department of Neurology and Neurosurgery 16 McGill University 17 Montréal, Québec, Canada 18 HJA 0BO 19 E-mail: [email protected] 20 21 Keywords: STK$%B, eSSential tremor, transcriptome, FUS 22 Conflict of Interests: All authors report no conflict of intereStS. 23 Funding Sources: Canadian InstituteS of Health ReSearch 24 bioRxiv preprint doi: https://doi.org/10.1101/552901; this version posted May 10, 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. -
Supplemental Solier
Supplementary Figure 1. Importance of Exon numbers for transcript downregulation by CPT Numbers of down-regulated genes for four groups of comparable size genes, differing only by the number of exons. Supplementary Figure 2. CPT up-regulates the p53 signaling pathway genes A, List of the GO categories for the up-regulated genes in CPT-treated HCT116 cells (p<0.05). In bold: GO category also present for the genes that are up-regulated in CPT- treated MCF7 cells. B, List of the up-regulated genes in both CPT-treated HCT116 cells and CPT-treated MCF7 cells (CPT 4 h). C, RT-PCR showing the effect of CPT on JUN and H2AFJ transcripts. Control cells were exposed to DMSO. β2 microglobulin (β2) mRNA was used as control. Supplementary Figure 3. Down-regulation of RNA degradation-related genes after CPT treatment A, “RNA degradation” pathway from KEGG. The genes with “red stars” were down- regulated genes after CPT treatment. B, Affy Exon array data for the “CNOT” genes. The log2 difference for the “CNOT” genes expression depending on CPT treatment was normalized to the untreated controls. C, RT-PCR showing the effect of CPT on “CNOT” genes down-regulation. HCT116 cells were treated with CPT (10 µM, 20 h) and CNOT6L, CNOT2, CNOT4 and CNOT6 mRNA were analysed by RT-PCR. Control cells were exposed to DMSO. β2 microglobulin (β2) mRNA was used as control. D, CNOT6L down-regulation after CPT treatment. CNOT6L transcript was analysed by Q- PCR. Supplementary Figure 4. Down-regulation of ubiquitin-related genes after CPT treatment A, “Ubiquitin-mediated proteolysis” pathway from KEGG.