DOCSLIB.ORG

Mouse Utp15 Conditional Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the GC Content Distribution Window size: 300 bp Sequence 12 Summary: Full Length(7362bp) | A(25.54% 1880) | C(19.65% 1447) | T(33.5% 2466) | G(21.31% 1569) Note: The sequence of homologous arms and cKO region is analyzed to determine the GC content. No significant high GC-content region is found. So this region is suitable for PCR screening or sequencing analysis. Page 3 of 7 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr13 - 98259717 98262716 3000 browser details YourSeq 88 1425 1528 3000 96.9% chr15 + 91620315 91620482 168 browser details YourSeq 60 2294 2488 3000 86.8% chr1 + 127685434 127900421 214988 browser details YourSeq 47 1418 1501 3000 96.1% chr10 + 11537557 11538109 553 browser details YourSeq 46 1460 1518 3000 98.0% chr2 + 89288507 89288671 165 browser details YourSeq 46 2289 2351 3000 91.3% chr15 + 62432390 62432454 65 browser details YourSeq 45 1493 1558 3000 75.0% chr3 - 36445372 36445423 52 browser details YourSeq 40 2289 2340 3000 93.5% chr10 + 95018901 95018954 54 browser details YourSeq 38 1466 1557 3000 69.1% chr2 + 161261882 161261939 58 browser details YourSeq 32 1460 1510 3000 74.4% chr3 - 92387528 92387570 43 browser details YourSeq 30 1470 1510 3000 76.5% chr1 + 153640881 153640915 35 browser details YourSeq 29 2289 2320 3000 96.8% chr5 - 146092444 146092476 33 browser details YourSeq 29 2291 2320 3000 100.0% chr12 + 102414331 102414361 31 browser details YourSeq 28 2461 2506 3000 91.0% chr12 - 21733356 21733401 46 browser details YourSeq 28 2292 2320 3000 100.0% chr4 + 138405807 138405836 30 browser details YourSeq 28 2409 2454 3000 96.8% chr2 + 119682765 119682816 52 browser details YourSeq 28 715 749 3000 91.5% chr15 + 32232719 32232754 36 browser details YourSeq 27 2289 2318 3000 96.7% chr4 - 121082281 121082311 31 browser details YourSeq 27 2291 2320 3000 96.6% chr10 - 31304463 31304493 31 browser details YourSeq 27 2290 2320 3000 96.6% chr7 + 116149340 116149371 32 Note: The 3000 bp section upstream of Exon 3 is BLAT searched against the genome. No significant similarity is found. BLAT Search Results (down) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr13 - 98255855 98258854 3000 browser details YourSeq 162 2432 2680 3000 87.5% chr11 + 68542028 68542218 191 browser details YourSeq 151 2496 2681 3000 89.5% chr4 + 41646759 41646932 174 browser details YourSeq 150 2502 2675 3000 95.3% chr17 + 46329442 46329618 177 browser details YourSeq 150 2499 2675 3000 93.4% chr11 + 88858068 88858242 175 browser details YourSeq 149 2499 2673 3000 91.1% chr3 + 37440843 37441010 168 browser details YourSeq 148 2499 2679 3000 90.2% chr12 - 24688321 24688496 176 browser details YourSeq 148 2498 2665 3000 92.0% chr11 - 106260035 106260195 161 browser details YourSeq 148 2499 2681 3000 90.6% chr1 - 173558552 173558729 178 browser details YourSeq 148 2501 2680 3000 89.6% chr15 + 100017950 100018122 173 browser details YourSeq 148 2496 2650 3000 98.1% chr14 + 57835025 57835184 160 browser details YourSeq 147 2496 2679 3000 92.5% chr11 - 62596545 62596728 184 browser details YourSeq 147 2499 2675 3000 92.9% chr3 + 146522408 146522583 176 browser details YourSeq 146 2499 2654 3000 96.8% chr2 + 53477003 53477158 156 browser details YourSeq 146 2498 2665 3000 91.9% chr11 + 106614062 106614222 161 browser details YourSeq 144 2499 2666 3000 91.3% chr2 + 129982663 129982823 161 browser details YourSeq 144 2499 2666 3000 91.3% chr17 + 26278698 26278858 161 browser details YourSeq 143 2493 2654 3000 94.5% chr11 - 73052189 73052355 167 browser details YourSeq 143 2495 2654 3000 95.6% chr12 + 69612018 69612188 171 browser details YourSeq 142 2499 2665 3000 92.4% chr12 - 76508606 76508767 162 Note: The 3000 bp section downstream of Exon 4 is BLAT searched against the genome. No significant similarity is found. Page 4 of 7 https://www.alphaknockout.com Gene and protein information: Utp15 UTP15 small subunit processome component [ Mus musculus (house mouse) ] Gene ID: 105372, updated on 12-Aug-2019 Gene summary Official Symbol Utp15 provided by MGI Official Full Name UTP15 small subunit processome component provided by MGI Primary source MGI:MGI:2145443 See related Ensembl:ENSMUSG00000041747 Gene type protein coding RefSeq status PROVISIONAL Organism Mus musculus Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Glires; Rodentia; Myomorpha; Muroidea; Muridae; Murinae; Mus; Mus Also known as AW544865 Expression Ubiquitous expression in CNS E11.5 (RPKM 7.4), placenta adult (RPKM 5.9) and 28 other tissues See more Orthologs human all Genomic context Location: 13; 13 D1 See Utp15 in Genome Data Viewer Exon count: 13 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 13 NC_000079.6 (98246845..98262992, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 13 NC_000079.5 (99016800..99032947, complement) Chromosome 13 - NC_000079.6 Page 5 of 7 https://www.alphaknockout.com Transcript information: This gene has 3 transcripts Gene: Utp15 ENSMUSG00000041747 Description UTP15 small subunit processome component [Source:MGI Symbol;Acc:MGI:2145443] Location Chromosome 13: 98,246,845-98,263,041 reverse strand. GRCm38:CM001006.2 About this gene This gene has 3 transcripts (splice variants), 198 orthologues and is a member of 1 Ensembl protein family. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Utp15-201 ENSMUST00000040972.3 4120 528aa ENSMUSP00000048204.2 Protein coding CCDS26712 Q8C7V3 TSL:1 GENCODE basic APPRIS P1 Utp15-202 ENSMUST00000224842.1 2328 No protein - Retained intron - - - Utp15-203 ENSMUST00000225100.1 634 No protein - Retained intron - - - 36.20 kb Forward strand 98.24Mb 98.25Mb 98.26Mb 98.27Mb Genes Ankra2-204 >protein coding (Comprehensive set... Ankra2-208 >protein coding Ankra2-207 >retained intron Ankra2-205 >protein coding Ankra2-202 >protein coding Ankra2-203 >protein coding Ankra2-201 >protein coding Ankra2-206 >retained intron Contigs AC123056.4 > Genes (Comprehensive set... < Utp15-201protein coding < Utp15-203retained intron < Utp15-202retained intron Regulatory Build 98.24Mb 98.25Mb 98.26Mb 98.27Mb Reverse strand 36.20 kb Regulation Legend CTCF Open Chromatin Promoter Promoter Flank Gene Legend Protein Coding Ensembl protein coding merged Ensembl/Havana Non-Protein Coding processed transcript Page 6 of 7 https://www.alphaknockout.com Transcript: ENSMUST00000040972 < Utp15-201protein coding Reverse strand 16.20 kb ENSMUSP00000048... MobiDB lite Low complexity (Seg) Superfamily WD40-repeat-containing domain superfamily SMART WD40 repeat Prints G-protein beta WD-40 repeat Pfam WD40 repeat U3 small nucleolar RNA-associated protein 15, C-terminal PROSITE profiles WD40-repeat-containing domain WD40 repeat PROSITE patterns WD40 repeat, conserved site PANTHER PTHR19924:SF30 PTHR19924 Gene3D WD40/YVTN repeat-like-containing domain superfamily CDD cd00200 All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant synonymous variant Scale bar 0 60 120 180 240 300 360 420 528 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 7 of 7.
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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Mouse Utp15 Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Utp15 Knockout Project (CRISPR/Cas9) Objective: To create a Utp15 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 2~10 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from the coding region. Exon 2~10 covers 72.35% of the coding region. The size of effective KO region: ~9042 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 6 7 8 9 10 13 Legends Exon of mouse Utp15 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 1904 bp section upstream of Exon 2 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 1010 bp section downstream of Exon 10 is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]