Mouse Stkld1 Conditional Knockout Project (CRISPR/Cas9)

Total Page:16

File Type:pdf, Size:1020Kb

Mouse Stkld1 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Stkld1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Stkld1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Stkld1 gene (NCBI Reference Sequence: NM_198628 ; Ensembl: ENSMUSG00000049897 ) is located on Mouse chromosome 2. 19 exons are identified, with the ATG start codon in exon 2 and the TGA stop codon in exon 19 (Transcript: ENSMUST00000055406). Exon 6 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Stkld1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-414L19 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 6 starts from about 15.31% of the coding region. The knockout of Exon 6 will result in frameshift of the gene. The size of intron 5 for 5'-loxP site insertion: 2015 bp, and the size of intron 6 for 3'-loxP site insertion: 675 bp. The size of effective cKO region: ~571 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 5 6 7 8 19 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Stkld1 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. Tandem repeats are found in the dot plot matrix. It may be difficult to construct this targeting vector. Overview of the GC Content Distribution Window size: 300 bp Sequence 12 Summary: Full Length(7071bp) | A(25.03% 1770) | C(24.1% 1704) | T(25.46% 1800) | G(25.41% 1797) 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% chr2 + 26939882 26942881 3000 browser details YourSeq 138 1232 1447 3000 86.4% chr4 + 77868593 77868813 221 browser details YourSeq 115 1280 1447 3000 91.5% chr6 + 87421896 87422079 184 browser details YourSeq 99 1250 1397 3000 84.4% chr19 + 5977047 5977188 142 browser details YourSeq 97 1252 1396 3000 86.1% chr11 + 22961750 22961890 141 browser details YourSeq 96 1273 1431 3000 87.1% chr10 - 42050193 42050766 574 browser details YourSeq 96 1280 1745 3000 93.0% chr10 + 29850749 29851320 572 browser details YourSeq 94 1266 1553 3000 90.5% chr15 + 76359515 76359946 432 browser details YourSeq 93 1252 1396 3000 84.3% chr18 - 3355858 3355998 141 browser details YourSeq 93 1252 1396 3000 83.2% chr6 + 116190434 116190573 140 browser details YourSeq 92 1233 1397 3000 83.0% chr11 - 46660232 46660390 159 browser details YourSeq 90 1280 1397 3000 90.3% chr5 - 139373882 139373996 115 browser details YourSeq 89 1280 1398 3000 88.3% chr1 - 16077295 16077412 118 browser details YourSeq 89 1189 1368 3000 86.3% chr18 + 79495021 79495583 563 browser details YourSeq 83 1272 1399 3000 86.8% chr2 - 128475175 128475300 126 browser details YourSeq 83 1252 1396 3000 83.7% chr4 + 85268575 85268715 141 browser details YourSeq 81 1251 1364 3000 92.8% chr15 + 80172611 80172726 116 browser details YourSeq 80 1287 1397 3000 87.7% chr6 + 52891188 52891295 108 browser details YourSeq 80 1282 1399 3000 84.0% chr10 + 84806597 84806711 115 browser details YourSeq 79 1282 1397 3000 88.4% chr8 - 94202933 94203051 119 Note: The 3000 bp section upstream of Exon 6 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% chr2 + 26943453 26946452 3000 browser details YourSeq 189 1093 1353 3000 91.0% chr10 - 61525524 61525985 462 browser details YourSeq 174 1111 1328 3000 90.7% chr11 - 42476360 42956679 480320 browser details YourSeq 154 1092 1259 3000 96.5% chr11 - 52674676 52674854 179 browser details YourSeq 152 1091 1264 3000 94.8% chr1 - 37249281 37249464 184 browser details YourSeq 151 1094 1344 3000 90.7% chr3 - 157583525 157583770 246 browser details YourSeq 150 1092 1273 3000 95.2% chr5 - 147751059 147751253 195 browser details YourSeq 150 1094 1352 3000 86.3% chr7 + 100589131 100589330 200 browser details YourSeq 148 1095 1329 3000 89.0% chr11 - 67023079 67023275 197 browser details YourSeq 145 1092 1253 3000 96.8% chr5 - 141235480 141235643 164 browser details YourSeq 145 1094 1253 3000 97.4% chr7 + 28531446 28531607 162 browser details YourSeq 144 959 1254 3000 91.2% chr5 - 107275292 107275586 295 browser details YourSeq 144 1092 1259 3000 96.8% chr1 - 34050507 34050685 179 browser details YourSeq 143 1093 1259 3000 93.9% chrX - 42230139 42230312 174 browser details YourSeq 143 1090 1250 3000 95.0% chr9 - 82892378 82892549 172 browser details YourSeq 143 1051 1252 3000 91.8% chr1 - 136888318 136888753 436 browser details YourSeq 143 1091 1254 3000 94.5% chr5 + 108691058 108691228 171 browser details YourSeq 143 1093 1254 3000 95.0% chr1 + 33723129 33723297 169 browser details YourSeq 142 1093 1353 3000 86.2% chr13 - 72868226 72868451 226 browser details YourSeq 142 1077 1254 3000 92.1% chr1 - 9807383 9807559 177 Note: The 3000 bp section downstream of Exon 6 is BLAT searched against the genome. No significant similarity is found. Page 4 of 7 https://www.alphaknockout.com Gene and protein information: Stkld1 serine/threonine kinase-like domain containing 1 [ Mus musculus (house mouse) ] Gene ID: 279029, updated on 12-Aug-2019 Gene summary Official Symbol Stkld1 provided by MGI Official Full Name serine/threonine kinase-like domain containing 1 provided by MGI Primary source MGI:MGI:2685557 See related Ensembl:ENSMUSG00000049897 Gene type protein coding RefSeq status VALIDATED Organism Mus musculus Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Glires; Rodentia; Myomorpha; Muroidea; Muridae; Murinae; Mus; Mus Also known as Gm711; Sgk071 Expression Biased expression in testis adult (RPKM 36.3), duodenum adult (RPKM 31.6) and 8 other tissues See more Orthologs human all Genomic context Location: 2; 2 A3 See Stkld1 in Genome Data Viewer Exon count: 22 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 2 NC_000068.7 (26933521..26953496) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 2 NC_000068.6 (26789589..26809016) Chromosome 2 - NC_000068.7 Page 5 of 7 https://www.alphaknockout.com Transcript information: This gene has 2 transcripts Gene: Stkld1 ENSMUSG00000049897 Description serine/threonine kinase-like domain containing 1 [Source:MGI Symbol;Acc:MGI:2685557] Gene Synonyms Gm711, LOC279029 Location Chromosome 2: 26,934,047-26,953,496 forward strand. GRCm38:CM000995.2 About this gene This gene has 2 transcripts (splice variants), 106 orthologues and is a member of 1 Ensembl protein family. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Stkld1-201 ENSMUST00000055406.8 2169 662aa ENSMUSP00000062967.8 Protein coding CCDS15818 Q80YS9 TSL:1 GENCODE basic APPRIS P1 Stkld1-202 ENSMUST00000153771.7 400 116aa ENSMUSP00000121332.1 Protein coding - B0R044 CDS 3' incomplete TSL:5 39.45 kb Forward strand 26.93Mb 26.94Mb 26.95Mb 26.96Mb Genes (Comprehensive set... Stkld1-202 >protein coding Stkld1-201 >protein coding Contigs AL773563.12 > Genes < Surf4-201protein coding < Rexo4-201protein coding (Comprehensive set... < Surf4-202nonsense mediated decay < Rexo4-202protein coding < Surf4-203protein coding < Rexo4-203lncRNA < Rexo4-205lncRNA < Rexo4-209lncRNA < Rexo4-207protein coding < Rexo4-208lncRNA < Rexo4-206lncRNA < Rexo4-204lncRNA Regulatory Build 26.93Mb 26.94Mb 26.95Mb 26.96Mb Reverse strand 39.45 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Gene Legend Protein Coding merged Ensembl/Havana Ensembl protein coding Non-Protein Coding RNA gene processed transcript Page 6 of 7 https://www.alphaknockout.com Transcript: ENSMUST00000055406 19.43 kb Forward strand Stkld1-201 >protein coding ENSMUSP00000062... MobiDB lite Low complexity (Seg) Superfamily Protein kinase-like domain superfamily Armadillo-type fold Pfam Protein kinase domain PROSITE profiles Protein kinase domain PANTHER PTHR24363 PTHR24363:SF5 Gene3D 1.10.510.10 CDD cd00180 All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant splice region variant synonymous variant Scale bar 0 60 120 180 240 300 360 420 480 540 600 662 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 7 of 7.
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.
    [Show full text]
  • Integrated Analysis of Differentially Expressed Genes in Breast Cancer Pathogenesis
    2560 ONCOLOGY LETTERS 9: 2560-2566, 2015 Integrated analysis of differentially expressed genes in breast cancer pathogenesis DAOBAO CHEN and HONGJIAN YANG Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, P.R. China Received October 20, 2014; Accepted March 10, 2015 DOI: 10.3892/ol.2015.3147 Abstract. The present study aimed to detect the differences ducts or from the lobules that supply the ducts (1). Breast between breast cancer cells and normal breast cells, and inves- cancer affects ~1.2 million women worldwide and accounts tigate the potential pathogenetic mechanisms of breast cancer. for ~50,000 mortalities every year (2). Despite major advances The sample GSE9574 series was downloaded, and the micro- in surgical and nonsurgical management of the disease, breast array data was analyzed to identify differentially expressed cancer metastasis remains a significant clinical challenge genes (DEGs). Gene Ontology (GO) cluster analysis using affecting numerous of patients (3). The prognosis and survival the GO Enrichment Analysis Software Toolkit platform and rates for breast cancer are highly variable, and depend on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway the cancer type, treatment strategy, stage of the disease and analysis for DEGs was conducted using the Gene Set Analysis geographical location of the patient (4). Toolkit V2. In addition, a protein-protein interaction (PPI) Microarray technology, which may be used to simultane- network was constructed, and target sites of potential transcrip- ously interrogate 10,000-40,000 genes, has provided new tion factors and potential microRNA (miRNA) molecules were insight into the molecular classification of different cancer screened.
    [Show full text]
  • Nucleolin and Its Role in Ribosomal Biogenesis
    NUCLEOLIN: A NUCLEOLAR RNA-BINDING PROTEIN INVOLVED IN RIBOSOME BIOGENESIS Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf vorgelegt von Julia Fremerey aus Hamburg Düsseldorf, April 2016 2 Gedruckt mit der Genehmigung der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf Referent: Prof. Dr. A. Borkhardt Korreferent: Prof. Dr. H. Schwender Tag der mündlichen Prüfung: 20.07.2016 3 Die vorgelegte Arbeit wurde von Juli 2012 bis März 2016 in der Klinik für Kinder- Onkologie, -Hämatologie und Klinische Immunologie des Universitätsklinikums Düsseldorf unter Anleitung von Prof. Dr. A. Borkhardt und in Kooperation mit dem ‚Laboratory of RNA Molecular Biology‘ an der Rockefeller Universität unter Anleitung von Prof. Dr. T. Tuschl angefertigt. 4 Dedicated to my family TABLE OF CONTENTS 5 TABLE OF CONTENTS TABLE OF CONTENTS ............................................................................................... 5 LIST OF FIGURES ......................................................................................................10 LIST OF TABLES .......................................................................................................12 ABBREVIATION .........................................................................................................13 ABSTRACT ................................................................................................................19 ZUSAMMENFASSUNG
    [Show full text]
  • Differences in Aggressive Behavior and DNA Copy Number Variants Between BALB/Cj and BALB/Cbyj Substrains
    Behav Genet (2010) 40:201–210 DOI 10.1007/s10519-009-9325-5 ORIGINAL RESEARCH Differences in Aggressive Behavior and DNA Copy Number Variants Between BALB/cJ and BALB/cByJ Substrains Lady Velez • Greta Sokoloff • Klaus A. Miczek • Abraham A. Palmer • Stephanie C. Dulawa Received: 3 February 2009 / Accepted: 3 December 2009 / Published online: 23 December 2009 Ó Springer Science+Business Media, LLC 2009 Abstract Some BALB/c substrains exhibit different Keywords Aggression Á Resident intruder Á BALB/c Á levels of aggression. We compared aggression levels CNV Á Substrain between male BALB/cJ and BALB/cByJ substrains using the resident intruder paradigm. These substrains were also assessed in other tests of emotionality and information Introduction processing including the open field, forced swim, fear conditioning, and prepulse inhibition tests. We also eval- Some substrains of BALB/c mice have previously been uated single nucleotide polymorphisms (SNPs) previously suggested to exhibit robust differences in aggressive reported between these BALB/c substrains. Finally, we behavior, although the genetic basis for this behavioral dif- compared BALB/cJ and BALB/cByJ mice for genomic ference has not been identified (Ciaranello et al. 1974). The deletions or duplications, collectively termed copy number BALB/c substrains were derived from an initial BALB/c variants (CNVs), to identify candidate genes that might stock established by 1935 (Les 1990); this BALB/c stock underlie the observed behavioral differences. BALB/cJ was then acquired by several other laboratories and were mice showed substantially higher aggression levels than maintained and bred as independent stocks including BALB/ BALB/cByJ mice; however, only minor differences in cJ, BALB/cN, and BALB/cByJ.
    [Show full text]
  • Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma
    Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen Submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in the Graduate School of Arts and Sciences Columbia University 2013 ! 2013 James Chen All rights reserved ABSTRACT Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma. TABLE OF CONTENTS CHAPTER 1… Introduction and Background 1 Glioblastoma and the Mesenchymal Subtype 3 Systems Biology and Master Regulators 9 Thesis Project: Genetics and Genomics 20 CHAPTER 2… TCGA Data Processing 23 CHAPTER 3… DIGGIn Part 1 – Selecting f-CNVs 33 Mutual Information 40 Application and Analysis 45 CHAPTER 4… DIGGIn Part 2 – Selecting drivers 52 CHAPTER 5… KLHL9 Manuscript 63 Methods 90 CHAPTER 5a… Revisions work-in-progress 105 CHAPTER 6… Discussion 109 APPENDICES… 132 APPEND01 – TCGA classifications 133 APPEND02 – GBM f-CNV list 136 APPEND03 – MES f-CNV candidate drivers 152 APPEND04 – Scripts 149 APPEND05 – Manuscript Figures and Legends 175 APPEND06 – Manuscript Supplemental Materials 185 i ACKNOWLEDGEMENTS I would like to thank the Califano Lab and my mentor, Andrea Califano, for their intellectual and motivational support during my stay in their lab.
    [Show full text]
  • Quantitative Proteomic Characterization and Comparison of T Helper 17 and Induced Regulatory T Cells
    METHODS AND RESOURCES Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells Imran Mohammad1,2, Kari Nousiainen3, Santosh D. Bhosale1,2, Inna Starskaia1,2, Robert Moulder1, Anne Rokka1, Fang Cheng4, Ponnuswamy Mohanasundaram4, John E. Eriksson4, David R. Goodlett5, Harri LaÈhdesmaÈki3, Zhi Chen1* 1 Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland, 2 Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland, 3 Department of Computer a1111111111 Science, Aalto University, Espoo, Finland, 4 Cell Biology, Biosciences, Faculty of Science and Engineering, a1111111111 Åbo Akademi University, Turku, Finland, 5 Department of Pharmaceutical Sciences, University of Maryland a1111111111 School of Pharmacy, Baltimore, Maryland, United States of America a1111111111 a1111111111 * [email protected] Abstract OPEN ACCESS The transcriptional network and protein regulators that govern T helper 17 (Th17) cell differ- Citation: Mohammad I, Nousiainen K, Bhosale SD, entiation have been studied extensively using advanced genomic approaches. For a better Starskaia I, Moulder R, Rokka A, et al. (2018) understanding of these biological processes, we have moved a step forward, from gene- to Quantitative proteomic characterization and protein-level characterization of Th17 cells. Mass spectrometry±based label-free quantita- comparison of T helper 17 and induced regulatory T cells. PLoS Biol 16(5): e2004194. https://doi.org/ tive (LFQ) proteomics analysis were made of in vitro differentiated murine Th17 and induced 10.1371/journal.pbio.2004194 regulatory T (iTreg) cells. More than 4,000 proteins, covering almost all subcellular compart- Academic Editor: Paula Oliver, University of ments, were detected. Quantitative comparison of the protein expression profiles resulted in Pennsylvania Perelman School of Medicine, United the identification of proteins specifically expressed in the Th17 and iTreg cells.
    [Show full text]
  • Sleep Alterations in Mouse Genetic Models of Human Disease
    University of Kentucky UKnowledge Theses and Dissertations--Biology Biology 2016 SLEEP ALTERATIONS IN MOUSE GENETIC MODELS OF HUMAN DISEASE Mansi Sethi University of Kentucky, [email protected] Author ORCID Identifier: http://orcid.org/0000-0002-6636-171X Digital Object Identifier: https://doi.org/10.13023/ETD.2016.522 Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Sethi, Mansi, "SLEEP ALTERATIONS IN MOUSE GENETIC MODELS OF HUMAN DISEASE" (2016). Theses and Dissertations--Biology. 38. https://uknowledge.uky.edu/biology_etds/38 This Doctoral Dissertation is brought to you for free and open access by the Biology at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Biology by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known.
    [Show full text]
  • Inbred Mouse Strains Expression in Primary Immunocytes Across
    Downloaded from http://www.jimmunol.org/ by guest on September 26, 2021 Daphne is online at: average * The Journal of Immunology published online 29 September 2014 from submission to initial decision 4 weeks from acceptance to publication Sara Mostafavi, Adriana Ortiz-Lopez, Molly A. Bogue, Kimie Hattori, Cristina Pop, Daphne Koller, Diane Mathis, Christophe Benoist, The Immunological Genome Consortium, David A. Blair, Michael L. Dustin, Susan A. Shinton, Richard R. Hardy, Tal Shay, Aviv Regev, Nadia Cohen, Patrick Brennan, Michael Brenner, Francis Kim, Tata Nageswara Rao, Amy Wagers, Tracy Heng, Jeffrey Ericson, Katherine Rothamel, Adriana Ortiz-Lopez, Diane Mathis, Christophe Benoist, Taras Kreslavsky, Anne Fletcher, Kutlu Elpek, Angelique Bellemare-Pelletier, Deepali Malhotra, Shannon Turley, Jennifer Miller, Brian Brown, Miriam Merad, Emmanuel L. Gautier, Claudia Jakubzick, Gwendalyn J. Randolph, Paul Monach, Adam J. Best, Jamie Knell, Ananda Goldrath, Vladimir Jojic, J Immunol http://www.jimmunol.org/content/early/2014/09/28/jimmun ol.1401280 Koller, David Laidlaw, Jim Collins, Roi Gazit, Derrick J. Rossi, Nidhi Malhotra, Katelyn Sylvia, Joonsoo Kang, Natalie A. Bezman, Joseph C. Sun, Gundula Min-Oo, Charlie C. Kim and Lewis L. Lanier Variation and Genetic Control of Gene Expression in Primary Immunocytes across Inbred Mouse Strains Submit online. Every submission reviewed by practicing scientists ? is published twice each month by http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2014/09/28/jimmunol.140128 0.DCSupplemental Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2014 by The American Association of Immunologists, Inc.
    [Show full text]
  • Title 1 Transcriptomic Analysis of Ribosome Biogenesis and Pre-Rrna
    bioRxiv preprint doi: https://doi.org/10.1101/2021.08.01.454488; this version posted August 1, 2021. 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 Title 2 Transcriptomic analysis of ribosome biogenesis and pre-rRNA processing during growth 3 stress in Entamoeba histolytica ∗1 4 Sarah Naiyer ,2, Shashi Shekhar Singh2,5, Devinder Kaur2,6, Yatendra Pratap Singh2, Amartya 5 Mukherjee2,3, Alok Bhattacharya4 and Sudha Bhattacharya2,4. 6 2- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India-110067 7 3- Present Address: Department of Molecular Reproduction, Development and Genetics, Indian 8 Institute of Science Bangalore-560012 9 4- Present Address: Ashoka University, Rajiv Gandhi Education City, Sonipat, Haryana, India - 10 131029 11 5- Present Address: Department of inflammation and Immunity, Cleveland Clinic, Cleveland, 12 OH, USA- 44195 13 6- Present Address: Central University of Punjab, Bathinda- 151401 14 15 16 17 *-Corresponding author 18 1- Present Address 19 Sarah Naiyer, PhD 20 Department of Immunology and Microbiology 21 University of Illinois at Chicago, USA, 60612 22 Email: [email protected], [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.01.454488; this version posted August 1, 2021. 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]
  • Initiation of Antiviral B Cell Immunity Relies on Innate Signals from Spatially Positioned NKT Cells
    Initiation of Antiviral B Cell Immunity Relies on Innate Signals from Spatially Positioned NKT Cells The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Gaya, Mauro et al. “Initiation of Antiviral B Cell Immunity Relies on Innate Signals from Spatially Positioned NKT Cells.” Cell 172, 3 (January 2018): 517–533 © 2017 The Author(s) As Published http://dx.doi.org/10.1016/j.cell.2017.11.036 Publisher Elsevier Version Final published version Citable link http://hdl.handle.net/1721.1/113555 Terms of Use Creative Commons Attribution 4.0 International License Detailed Terms http://creativecommons.org/licenses/by/4.0/ Article Initiation of Antiviral B Cell Immunity Relies on Innate Signals from Spatially Positioned NKT Cells Graphical Abstract Authors Mauro Gaya, Patricia Barral, Marianne Burbage, ..., Andreas Bruckbauer, Jessica Strid, Facundo D. Batista Correspondence [email protected] (M.G.), [email protected] (F.D.B.) In Brief NKT cells are required for the initial formation of germinal centers and production of effective neutralizing antibody responses against viruses. Highlights d NKT cells promote B cell immunity upon viral infection d NKT cells are primed by lymph-node-resident macrophages d NKT cells produce early IL-4 wave at the follicular borders d Early IL-4 wave is required for efficient seeding of germinal centers Gaya et al., 2018, Cell 172, 517–533 January 25, 2018 ª 2017 The Authors. Published by Elsevier Inc. https://doi.org/10.1016/j.cell.2017.11.036 Article Initiation of Antiviral B Cell Immunity Relies on Innate Signals from Spatially Positioned NKT Cells Mauro Gaya,1,2,* Patricia Barral,2,3 Marianne Burbage,2 Shweta Aggarwal,2 Beatriz Montaner,2 Andrew Warren Navia,1,4,5 Malika Aid,6 Carlson Tsui,2 Paula Maldonado,2 Usha Nair,1 Khader Ghneim,7 Padraic G.
    [Show full text]
  • A Novel 2.3 Mb Microduplication of 9Q34. 3 Inserted Into 19Q13. 4 in A
    Hindawi Publishing Corporation Case Reports in Pediatrics Volume 2012, Article ID 459602, 7 pages doi:10.1155/2012/459602 Case Report A Novel 2.3 Mb Microduplication of 9q34.3 Inserted into 19q13.4 in a Patient with Learning Disabilities Shalinder Singh,1 Fern Ashton,1 Renate Marquis-Nicholson,1 Jennifer M. Love,1 Chuan-Ching Lan,1 Salim Aftimos,2 Alice M. George,1 and Donald R. Love1, 3 1 Diagnostic Genetics, LabPlus, Auckland City Hospital, P.O. Box 110031, Auckland 1148, New Zealand 2 Genetic Health Service New Zealand-Northern Hub, Auckland City Hospital, Private Bag 92024, Auckland 1142, New Zealand 3 School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand Correspondence should be addressed to Donald R. Love, [email protected] Received 1 July 2012; Accepted 27 September 2012 Academic Editors: L. Cvitanovic-Sojat, G. Singer, and V. C. Wong Copyright © 2012 Shalinder Singh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Insertional translocations in which a duplicated region of one chromosome is inserted into another chromosome are very rare. We report a 16.5-year-old girl with a terminal duplication at 9q34.3 of paternal origin inserted into 19q13.4. Chromosomal analysis revealed the karyotype 46,XX,der(19)ins(19;9)(q13.4;q34.3q34.3)pat. Cytogenetic microarray analysis (CMA) identified a ∼2.3Mb duplication of 9q34.3 → qter, which was confirmed by Fluorescence in situ hybridisation (FISH).
    [Show full text]
  • Supplementary Appendix Table of Contents
    Supplementary Appendix This appendix has been provided by the authors to give readers additional information about their work. Table of Contents Supplementary Note ........................................................................................................................... 1 Author contributions ............................................................................................................................... 1 Additional authors from study groups .............................................................................................. 2 Task Force COVID-19 Humanitas ......................................................................................................................................... 2 TASK FORCE COVID-19 HUMANITAS GAVAZZENI & CASTELLI ............................................................................. 3 COVICAT Study Group ............................................................................................................................................................... 4 Pa COVID-19 Study Group ....................................................................................................................................................... 5 Covid-19 Aachen Study (COVAS) .......................................................................................................................................... 5 Norwegian SARS-CoV-2 Study group .................................................................................................................................
    [Show full text]