Mouse Sec14l2 Conditional Knockout Project (CRISPR/Cas9)

Total Page:16

File Type:pdf, Size:1020Kb

Mouse Sec14l2 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Sec14l2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Sec14l2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Sec14l2 gene (NCBI Reference Sequence: NM_144520 ; Ensembl: ENSMUSG00000003585 ) is located on Mouse chromosome 11. 12 exons are identified, with the ATG start codon in exon 1 and the TAA stop codon in exon 12 (Transcript: ENSMUST00000003681). Exon 2 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Sec14l2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-81P12 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: Mice homozygous for a null allele exhibit decreased cholesterol synthesis and plasma levels under fasting conditions compared to wild-type mice. Exon 2 starts from about 4.55% of the coding region. The knockout of Exon 2 will result in frameshift of the gene. The size of intron 1 for 5'-loxP site insertion: 1868 bp, and the size of intron 2 for 3'-loxP site insertion: 4743 bp. The size of effective cKO region: ~576 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 12 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Homology arm Exon of mouse Sec14l2 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(7076bp) | A(22.67% 1604) | C(24.46% 1731) | T(25.27% 1788) | G(27.6% 1953) Note: The sequence of homologous arms and cKO region is analyzed to determine the GC content. Significant high GC-content regions are found. It may be difficult to construct this targeting vector. 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% chr11 - 4117007 4120006 3000 browser details YourSeq 24 991 1017 3000 96.3% chr1 + 48980552 48980591 40 browser details YourSeq 23 818 844 3000 88.0% chr15 - 12682061 12682086 26 browser details YourSeq 23 2857 2879 3000 100.0% chr15 + 8303929 8303951 23 browser details YourSeq 22 2859 2880 3000 100.0% chr12 - 80875225 80875246 22 browser details YourSeq 22 1675 1696 3000 100.0% chr12 + 80258605 80258626 22 browser details YourSeq 22 717 739 3000 100.0% chr1 + 13615358 13615381 24 browser details YourSeq 21 1590 1618 3000 86.3% chr6 - 13975481 13975509 29 browser details YourSeq 21 2861 2881 3000 100.0% chr15 + 78194771 78194791 21 browser details YourSeq 21 2856 2876 3000 100.0% chr10 + 61045651 61045671 21 Note: The 3000 bp section upstream of Exon 2 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% chr11 - 4113431 4116430 3000 browser details YourSeq 193 2357 2579 3000 97.1% chr1 + 131115798 131116427 630 browser details YourSeq 191 2379 2582 3000 97.6% chr2 + 173349081 173349293 213 browser details YourSeq 189 2388 2602 3000 93.6% chr17 - 32261378 32261581 204 browser details YourSeq 187 2384 2598 3000 95.7% chr9 - 40814629 40814843 215 browser details YourSeq 186 2384 2580 3000 97.5% chr11 - 102744501 102744707 207 browser details YourSeq 185 2388 2582 3000 97.5% chr9 - 37591750 37591944 195 browser details YourSeq 185 2388 2581 3000 98.0% chr11 - 85562886 85563083 198 browser details YourSeq 185 2388 2583 3000 97.5% chr1 - 58471251 58471447 197 browser details YourSeq 184 2388 2583 3000 97.0% chr12 - 108230729 108230924 196 browser details YourSeq 184 2388 2582 3000 97.5% chr11 + 87413073 87413271 199 browser details YourSeq 183 2388 2580 3000 97.5% chr2 - 170448735 170448927 193 browser details YourSeq 183 2388 2579 3000 98.0% chr14 - 56772446 56772638 193 browser details YourSeq 183 2388 2579 3000 98.0% chr11 - 54798765 54798958 194 browser details YourSeq 183 2388 2582 3000 97.0% chr10 - 80661940 80662134 195 browser details YourSeq 183 2385 2579 3000 97.5% chr1 - 84919703 84919910 208 browser details YourSeq 183 2388 2580 3000 97.5% chr7 + 49463829 49464021 193 browser details YourSeq 183 2388 2580 3000 97.5% chr4 + 132769978 132770170 193 browser details YourSeq 183 2385 2579 3000 97.0% chr3 + 68486903 68487097 195 browser details YourSeq 183 2388 2580 3000 97.5% chr19 + 40345399 40345591 193 Note: The 3000 bp section downstream of Exon 2 is BLAT searched against the genome. No significant similarity is found. Page 4 of 7 https://www.alphaknockout.com Gene and protein information: Sec14l2 SEC14-like lipid binding 2 [ Mus musculus (house mouse) ] Gene ID: 67815, updated on 10-Oct-2019 Gene summary Official Symbol Sec14l2 provided by MGI Official Full Name SEC14-like lipid binding 2 provided by MGI Primary source MGI:MGI:1915065 See related Ensembl:ENSMUSG00000003585 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 Spf; TAP; 1300013M05Rik Expression Biased expression in liver adult (RPKM 140.8), liver E18 (RPKM 27.5) and 6 other tissues See more Orthologs human all Genomic context Location: 11; 11 A1 See Sec14l2 in Genome Data Viewer Exon count: 12 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 11 NC_000077.6 (4097039..4118729, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 11 NC_000077.5 (3997042..4018732, complement) Chromosome 11 - NC_000077.6 Page 5 of 7 https://www.alphaknockout.com Transcript information: This gene has 6 transcripts Gene: Sec14l2 ENSMUSG00000003585 Description SEC14-like lipid binding 2 [Source:MGI Symbol;Acc:MGI:1915065] Gene Synonyms 1300013M05Rik, Spf, tap Location Chromosome 11: 4,097,039-4,123,415 reverse strand. GRCm38:CM001004.2 About this gene This gene has 6 transcripts (splice variants), 197 orthologues, 5 paralogues, is a member of 1 Ensembl protein family and is associated with 2 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Sec14l2-201 ENSMUST00000003681.7 2631 403aa ENSMUSP00000003681.7 Protein coding CCDS24377 Q99J08 TSL:1 GENCODE basic APPRIS P1 Sec14l2-202 ENSMUST00000123901.7 1624 No protein - lncRNA - - TSL:5 Sec14l2-203 ENSMUST00000132421.7 1229 No protein - lncRNA - - TSL:5 Sec14l2-204 ENSMUST00000133631.1 880 No protein - lncRNA - - TSL:3 Sec14l2-205 ENSMUST00000136420.1 429 No protein - lncRNA - - TSL:2 Sec14l2-206 ENSMUST00000145173.1 423 No protein - lncRNA - - TSL:5 46.38 kb Forward strand 4.09Mb 4.10Mb 4.11Mb 4.12Mb 4.13Mb Genes Gm11955-201 >processed pseudogene (Comprehensive set... Contigs AL807395.8 > Genes (Comprehensive set... < Mtfp1-201protein codin<g Sec14l2-202lncRNA < Gm11957-201processed pseudogene < Mtfp1-202lncRNA < Sec14l2-204lncRNA < Sec14l2-206lncRNA < Sec14l2-201protein coding < Sec14l2-203lncRNA < Sec14l2-205lncRNA Regulatory Build 4.09Mb 4.10Mb 4.11Mb 4.12Mb 4.13Mb Reverse strand 46.38 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Gene Legend Protein Coding merged Ensembl/Havana Non-Protein Coding RNA gene pseudogene Page 6 of 7 https://www.alphaknockout.com Transcript: ENSMUST00000003681 < Sec14l2-201protein coding Reverse strand 21.79 kb ENSMUSP00000003... Superfamily CRAL-TRIO lipid binding domain superfamily GOLD domain superfamily CRAL/TRIO, N-terminal domain superfamily SMART CRAL/TRIO, N-terminal domain CRAL-TRIO lipid binding domain Prints PR00180 Pfam CRAL-TRIO lipid binding domain PROSITE profiles CRAL-TRIO lipid binding domain GOLD domain PANTHER PTHR23324:SF64 PTHR23324 Gene3D CRAL-TRIO lipid binding domain superfamily 2.60.120.680 CDD CRAL-TRIO lipid binding domain All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant synonymous variant Scale bar 0 40 80 120 160 200 240 280 320 360 403 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 7 of 7.
Recommended publications
  • Análise Integrativa De Perfis Transcricionais De Pacientes Com
    UNIVERSIDADE DE SÃO PAULO FACULDADE DE MEDICINA DE RIBEIRÃO PRETO PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Ribeirão Preto – 2012 ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Tese apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo para obtenção do título de Doutor em Ciências. Área de Concentração: Genética Orientador: Prof. Dr. Eduardo Antonio Donadi Co-orientador: Prof. Dr. Geraldo A. S. Passos Ribeirão Preto – 2012 AUTORIZO A REPRODUÇÃO E DIVULGAÇÃO TOTAL OU PARCIAL DESTE TRABALHO, POR QUALQUER MEIO CONVENCIONAL OU ELETRÔNICO, PARA FINS DE ESTUDO E PESQUISA, DESDE QUE CITADA A FONTE. FICHA CATALOGRÁFICA Evangelista, Adriane Feijó Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. Ribeirão Preto, 2012 192p. Tese de Doutorado apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Área de Concentração: Genética. Orientador: Donadi, Eduardo Antonio Co-orientador: Passos, Geraldo A. 1. Expressão gênica – microarrays 2. Análise bioinformática por module maps 3. Diabetes mellitus tipo 1 4. Diabetes mellitus tipo 2 5. Diabetes mellitus gestacional FOLHA DE APROVAÇÃO ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas.
    [Show full text]
  • Identification of Transcriptional Mechanisms Downstream of Nf1 Gene Defeciency in Malignant Peripheral Nerve Sheath Tumors Daochun Sun Wayne State University
    Wayne State University DigitalCommons@WayneState Wayne State University Dissertations 1-1-2012 Identification of transcriptional mechanisms downstream of nf1 gene defeciency in malignant peripheral nerve sheath tumors Daochun Sun Wayne State University, Follow this and additional works at: http://digitalcommons.wayne.edu/oa_dissertations Recommended Citation Sun, Daochun, "Identification of transcriptional mechanisms downstream of nf1 gene defeciency in malignant peripheral nerve sheath tumors" (2012). Wayne State University Dissertations. Paper 558. This Open Access Dissertation is brought to you for free and open access by DigitalCommons@WayneState. It has been accepted for inclusion in Wayne State University Dissertations by an authorized administrator of DigitalCommons@WayneState. IDENTIFICATION OF TRANSCRIPTIONAL MECHANISMS DOWNSTREAM OF NF1 GENE DEFECIENCY IN MALIGNANT PERIPHERAL NERVE SHEATH TUMORS by DAOCHUN SUN DISSERTATION Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2012 MAJOR: MOLECULAR BIOLOGY AND GENETICS Approved by: _______________________________________ Advisor Date _______________________________________ _______________________________________ _______________________________________ © COPYRIGHT BY DAOCHUN SUN 2012 All Rights Reserved DEDICATION This work is dedicated to my parents and my wife Ze Zheng for their continuous support and understanding during the years of my education. I could not achieve my goal without them. ii ACKNOWLEDGMENTS I would like to express tremendous appreciation to my mentor, Dr. Michael Tainsky. His guidance and encouragement throughout this project made this dissertation come true. I would also like to thank my committee members, Dr. Raymond Mattingly and Dr. John Reiners Jr. for their sustained attention to this project during the monthly NF1 group meetings and committee meetings, Dr.
    [Show full text]
  • Science Journals
    SCIENCE ADVANCES | RESEARCH ARTICLE VIROLOGY Copyright © 2020 The Authors, some rights reserved; Liver-expressed Cd302 and Cr1l limit hepatitis C virus exclusive licensee American Association cross-species transmission to mice for the Advancement Richard J. P. Brown1,2*, Birthe Tegtmeyer2, Julie Sheldon2, Tanvi Khera2,3, Anggakusuma2,4, of Science. No claim to 2,5,6 2,7 2 2 2 original U.S. Government Daniel Todt , Gabrielle Vieyres , Romy Weller , Sebastian Joecks , Yudi Zhang , Works. Distributed 2 2 2 2 2,5 Svenja Sake , Dorothea Bankwitz , Kathrin Welsch , Corinne Ginkel , Michael Engelmann , under a Creative 8,9 2,5 10,11 10,11 Gisa Gerold , Eike Steinmann , Qinggong Yuan , Michael Ott , Commons Attribution Florian W. R. Vondran12,13, Thomas Krey13,14,15,16,17, Luisa J. Ströh14, Csaba Miskey18, NonCommercial Zoltán Ivics18, Vanessa Herder19, Wolfgang Baumgärtner19, Chris Lauber2,20, Michael Seifert20, License 4.0 (CC BY-NC). Alexander W. Tarr21,22, C. Patrick McClure21,22, Glenn Randall23, Yasmine Baktash24, Alexander Ploss25, Viet Loan Dao Thi26,27, Eleftherios Michailidis27, Mohsan Saeed26,28, Lieven Verhoye29, Philip Meuleman29, Natascha Goedecke30, Dagmar Wirth30,31, Charles M. Rice26, Thomas Pietschmann2,13,15* Downloaded from Hepatitis C virus (HCV) has no animal reservoir, infecting only humans. To investigate species barrier determinants limiting infection of rodents, murine liver complementary DNA library screening was performed, identifying transmembrane proteins Cd302 and Cr1l as potent restrictors of HCV propagation. Combined ectopic expression in human hepatoma cells impeded HCV uptake and cooperatively mediated transcriptional dysregulation of a noncanonical program of immunity genes. Murine hepatocyte expression of both factors was constitutive and not interferon inducible, while differences in liver expression and the ability to restrict HCV were observed between http://advances.sciencemag.org/ the murine orthologs and their human counterparts.
    [Show full text]
  • One Novel MR, SEC14L2 Inhibits Cancer Cells
    www.aging-us.com AGING 2019, Vol. 11, No. 24 Research Paper Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells Zhihui Li1,*, Yi Lou1,2,*, Guoyan Tian1, Jianyue Wu1, Anqian Lu1, Jin Chen1, Beibei Xu1, Junping Shi1, Jin Yang1 1Translational Medicine Center, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang 310015, P.R. China 2Department of Occupational Medicine, Zhejiang Provincial Integrated Chinese and Western Medicine Hospital, Hangzhou, Zhejiang 310003, P.R. China *Equal contribution Correspondence to: Junping Shi, Jin Yang; email: [email protected], [email protected] Keywords: liver cancer, master regulator, transcriptional network, SEC14L2 Received: June 28, 2019 Accepted: November 26, 2019 Published: December 18, 2019 Copyright: Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Identification of master regulator (MR) genes offers a relatively rapid and efficient way to characterize disease-specific molecular programs. Since strong consensus regarding commonly altered MRs in hepatocellular carcinoma (HCC) is lacking, we generated a compendium of HCC datasets from 21 studies and identified a comprehensive signature consisting of 483 genes commonly deregulated in HCC. We then used reverse engineering of transcriptional networks to identify the MRs that underpin the development and progression of HCC. After cross-validation in different HCC datasets, systematic assessment using patient-derived data confirmed prognostic predictive capacities for most HCC MRs and their corresponding regulons. Our HCC signature covered well-established liver cancer hallmarks, and network analyses revealed coordinated interaction between several MRs.
    [Show full text]
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
    [Show full text]
  • Epigenetic Marks on Grandchildren´S Growth, Glucoregulatory and Stress 3 Genes
    bioRxiv preprint doi: https://doi.org/10.1101/215467; this version posted December 13, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Bygren & al. Early life Feast and Famine: The Methylome of disparate stressors inherited 1 Title: Paternal grandparental exposure to crop failure or surfeit during a childhood slow 2 growth period: Epigenetic marks on grandchildren´s growth, glucoregulatory and stress 3 genes 4 Authors: Lars Olov Bygren1,2*, Patrick Müller1, David Brodin1, Gunnar.Kaati1, Jan-Åke 5 Gustafsson1, John G. Kral3 6 Affiliations: 7 1 Department of Biosciences and Nutrition, Karolinska Institutet, SE_14183 Huddinge, 8 Sweden. 9 2 Departments of Community Medicine and Rehabilitation, Umeå University, SE_90187, 10 Umeå, Sweden. 11 3 Departments of Surgery, Medicine, Cell Biology, SUNY Downstate Medical Center, 450 12 Clarkson Avenue, Box 40, Brooklyn, NY 11203, USA. 13 * Correspondence to: [email protected] 14 ABSTRACT 15 This latest in our series of papers describes transgenerational methylation related to mid- 16 childhood food availability in 19th century Överkalix, Sweden. Failed vs. bountiful crops 17 differentially influenced methylation in grandchildren of paternal grandparents exposed to 18 feast or famine during their Slow Growth Period (SGP), a sensitive period preceding the pre- 1 bioRxiv preprint doi: https://doi.org/10.1101/215467; this version posted December 13, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Bygren & al. Early life Feast and Famine: The Methylome of disparate stressors inherited 19 pubertal growth spurt.
    [Show full text]
  • Supplemental Material 1
    Supplemental material 1 Genes TCGA Genes TCGA Name Name early BC advanced BC phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic TP53 tumor protein p53 PIK3CA subunit alpha phosphatidylinositol-4,5-bisphosphate 3-kinase, PIK3CA TP53 tumor protein p53 catalytic subunit alpha CDH1 cadherin 1, type 1, E-cadherin (epithelial) CLIP1 CAP-GLY domain containing linker protein 1 GATA3 GATA binding protein 3 MUC1 mucin 1, cell surface associated KMT2C lysine (K)-specific methyltransferase 2C TSHR thyroid stimulating hormone receptor mitogen-activated protein kinase kinase kinase MAP3K1 FGFR2 fibroblast growth factor receptor 2 1, E3 ubiquitin protein ligase PTEN phosphatase and tensin homolog CBLB Cbl proto-oncogene B, E3 ubiquitin protein ligase NCOR1 nuclear receptor corepressor 1 IL21R interleukin 21 receptor NF1 neurofibromin 1 AMER1 APC membrane recruitment protein 1 RUNX1 runt-related transcription factor 1 NSD1 nuclear receptor binding SET domain protein 1 ARID1A AT rich interactive domain 1A (SWI-like) ETNK1 ethanolamine kinase 1 phosphoinositide-3-kinase, regulatory subunit 1 PIK3R1 AKAP9 A kinase (PRKA) anchor protein 9 (alpha) MAP2K4 mitogen-activated protein kinase kinase 4 PTPRB protein tyrosine phosphatase, receptor type, B RNF213 ring finger protein 213 ERBB2 erb-b2 receptor tyrosine kinase 2 KMT2D lysine (K)-specific methyltransferase 2D NOTCH1 notch 1 SPEN spen family transcriptional repressor TPR translocated promoter region, nuclear basket protein AKAP9 A kinase (PRKA) anchor protein 9 FLI1 Fli-1 proto-oncogene, ETS transcription
    [Show full text]
  • The Contribution of Exon-Skipping Events on Chromosome 22 to Protein Coding Diversity
    Downloaded from genome.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Letter The Contribution of Exon-Skipping Events on Chromosome 22 to Protein Coding Diversity Winston A. Hide,1,3 Vladimir N. Babenko,1,2 Peter A. van Heusden, Cathal Seoighe, and Janet F. Kelso South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa Completion of the human genome sequence provides evidence for a gene count with lower bound 30,000–40,000. Significant protein complexity may derive in part from multiple transcript isoforms. Recent EST based studies have revealed that alternate transcription, including alternative splicing, polyadenylation and transcription start sites, occurs within at least 30–40% of human genes. Transcript form surveys have yet to integrate the genomic context, expression, frequency, and contribution to protein diversity of isoform variation. We determine here the degree to which protein coding diversity may be influenced by alternate expression of transcripts by exhaustive manual confirmation of genome sequence annotation, and comparison to available transcript data to accurately associate skipped exon isoforms with genomic sequence. Relative expression levels of transcripts are estimated from EST database representation. The rigorous in silico method accurately identifies exon skipping using verified genome sequence. 545 genes have been studied in this first hand-curated assessment of exon skipping on chromosome 22. Combining manual assessment with software screening of exon boundaries provides a highly accurate and internally consistent indication of skipping frequency. 57 of 62 exon skipping events occur in the protein coding regions of 52 genes. A single gene, (FBXO7) expresses an exon repetition.
    [Show full text]
  • Additional File 1: SDS-PAGE Fractionation with Silver Staining of Bone Marrow Supernatant from Six Β- Thalassemia/Hb E Patients (P) and Four Donors (D)
    Additional file 1: SDS-PAGE fractionation with silver staining of bone marrow supernatant from six β- thalassemia/Hb E patients (P) and four donors (D). Uniprot KB P1 P2 P3 P4 P5 P6 D1 D2 D3 D4 CU025_HUMAN 6.53 5.98 5.59 6.25 7.38 8.09 6.8 5.81 7.81 6.94 CUL3_HUMAN 0 0 7.6 3.56 5.69 0 11.01 0 0 0 CUL4B_HUMAN 8.61 8.11 8.56 9.46 9.54 7.4 9.26 9 9.57 10.67 CUX2_HUMAN 1.8 6.51 2.99 5.02 2.29 1.98 2.74 2.61 2.11 2.8 CWC22_HUMAN 8.36 8.89 8.05 8.62 8.13 8.77 9.12 8.74 8.06 8.59 CYB5_HUMAN 5.78 9.72 5.26 9.47 0 5.77 0 9.68 0 10 D19L2_HUMAN 0 0 0 0 4.94 0 6.18 0 0 8.56 DAZP2_HUMAN 0 7.1 3.54 3.88 0 0 3.79 3.96 5.3 8.73 DCA15_HUMAN 7.53 7.25 7.02 6.11 9.86 7.84 5.82 3.91 6.34 6.15 DCD2C_HUMAN 5.71 6.39 5.29 6.49 8.37 9.08 5.91 6.57 7.8 0 DCLK2_HUMAN 9.4 9.82 8.48 10.6 0 0 9.76 8.47 0 0 DDX_HUMAN 9.65 9.54 9.63 9.79 9.78 9.82 9.68 9.78 9.96 9.86 DDX5_HUMAN 8.48 8.65 9.79 8.16 8.11 8.3 8.25 8.09 8.46 8.16 DGCR8_HUMAN 7.82 8.93 10.83 6.47 9.7 11.78 9.64 12.25 9.57 8.36 DHH_HUMAN 8.08 8.14 8.49 8.48 8.16 8.5 8.13 8.22 8.09 8.48 DHX30_HUMAN 7.53 6.29 6.37 5.6 0 0 10.08 6.27 0 0 DHX8_HUMAN 11.9 12.02 12.07 11.92 12.06 12.07 12.07 12.06 12.07 12.03 DLGP2_HUMAN 13.5 0 0 0 13.73 13.72 0 13.72 13.72 13.72 DMXL2_HUMAN 0 0 0 2.97 4.72 6.18 0 4.59 6.91 7.03 DNA2_HUMAN 5.51 4.76 3.47 3.31 3.68 4.53 3.67 5.95 4.11 6.19 DPOD3_HUMAN 0 8.42 0 0 0 8.19 0 0 0 8.86 DPYL4_HUMAN 6.11 4.67 3.91 7.93 6.53 9.88 7.66 2.81 5.1 4.63 DSCL1_HUMAN 0 0 0 7.25 0 7.07 0 0 0 7.87 DUS8_HUMAN 7.32 7.12 7.05 8.17 6.9 7.19 7.45 7.78 6.52 7.17 DYH17_HUMAN 6.25 0 5.34 0 4.32 7.65 6.05
    [Show full text]
  • Proteomic Landscape of the Human Choroid–Retinal Pigment Epithelial Complex
    Supplementary Online Content Skeie JM, Mahajan VB. Proteomic landscape of the human choroid–retinal pigment epithelial complex. JAMA Ophthalmol. Published online July 24, 2014. doi:10.1001/jamaophthalmol.2014.2065. eFigure 1. Choroid–retinal pigment epithelial (RPE) proteomic analysis pipeline. eFigure 2. Gene ontology (GO) distributions and pathway analysis of human choroid– retinal pigment epithelial (RPE) protein show tissue similarity. eMethods. Tissue collection, mass spectrometry, and analysis. eTable 1. Complete table of proteins identified in the human choroid‐RPE using LC‐ MS/MS. eTable 2. Top 50 signaling pathways in the human choroid‐RPE using MetaCore. eTable 3. Top 50 differentially expressed signaling pathways in the human choroid‐RPE using MetaCore. eTable 4. Differentially expressed proteins in the fovea, macula, and periphery of the human choroid‐RPE. eTable 5. Differentially expressed transcription proteins were identified in foveal, macular, and peripheral choroid‐RPE (p<0.05). eTable 6. Complement proteins identified in the human choroid‐RPE. eTable 7. Proteins associated with age related macular degeneration (AMD). This supplementary material has been provided by the authors to give readers additional information about their work. © 2014 American Medical Association. All rights reserved. 1 Downloaded From: https://jamanetwork.com/ on 09/25/2021 eFigure 1. Choroid–retinal pigment epithelial (RPE) proteomic analysis pipeline. A. The human choroid‐RPE was dissected into fovea, macula, and periphery samples. B. Fractions of proteins were isolated and digested. C. The peptide fragments were analyzed using multi‐dimensional LC‐MS/MS. D. X!Hunter, X!!Tandem, and OMSSA were used for peptide fragment identification. E. Proteins were further analyzed using bioinformatics.
    [Show full text]
  • Insights Into Expression, Cellular Localization, and Regulation of Supernatant Protein Factor, a Putative Regulator of Cholesterol Biosynthesis
    University of Kentucky UKnowledge University of Kentucky Doctoral Dissertations Graduate School 2009 INSIGHTS INTO EXPRESSION, CELLULAR LOCALIZATION, AND REGULATION OF SUPERNATANT PROTEIN FACTOR, A PUTATIVE REGULATOR OF CHOLESTEROL BIOSYNTHESIS Elzbieta Ilona Stolarczyk University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Stolarczyk, Elzbieta Ilona, "INSIGHTS INTO EXPRESSION, CELLULAR LOCALIZATION, AND REGULATION OF SUPERNATANT PROTEIN FACTOR, A PUTATIVE REGULATOR OF CHOLESTEROL BIOSYNTHESIS" (2009). University of Kentucky Doctoral Dissertations. 696. https://uknowledge.uky.edu/gradschool_diss/696 This Dissertation is brought to you for free and open access by the Graduate School at UKnowledge. It has been accepted for inclusion in University of Kentucky Doctoral Dissertations by an authorized administrator of UKnowledge. For more information, please contact [email protected]. ABSTRACT OF DISSERTATION Elzbieta Ilona Stolarczyk The Graduate School University of Kentucky 2009 INSIGHTS INTO EXPRESSION, CELLULAR LOCALIZATION, AND REGULATION OF SUPERNATANT PROTEIN FACTOR, A PUTATIVE REGULATOR OF CHOLESTEROL BIOSYNTHESIS ABSTRACT OF DISSERTATION A dissertation submitted in partial fulfillment of the requirements of the degree of Doctor of Philosophy in The Graduate School at the University of Kentucky By Elzbieta Ilona Stolarczyk Lexington, Kentucky Director: Dr. Todd D. Porter, Associate Professor, Pharmaceutical Sciences 2009 Copyright © Elzbieta Ilona Stolarczyk 2009 ABSTRACT OF DISSERTATION INSIGHTS INTO EXPRESSION, CELLULAR LOCALIZATION, AND REGULATION OF SUPERNATANT PROTEIN FACTOR, A PUTATIVE REGULATOR OF CHOLESTEROL BIOSYNTHESIS SPF (Supernatant Protein Factor) is a cytosolic protein that stimulates at least two enzymes in the cholesterol biosynthetic pathway: squalene monooxygenase and HMG- CoA reductase.
    [Show full text]
  • Identification of Novel Genes Associated with HIV-1 Latency By
    Kim et al. Human Genomics (2017) 11:9 DOI 10.1186/s40246-017-0105-7 LETTER TO THE EDITOR Open Access Identification of novel genes associated with HIV-1 latency by analysis of histone modifications Kyung-Chang Kim*, Sunyoung Lee, Junseock Son, Younghyun Shin, Cheol-Hee Yoon, Chun Kang and Byeong-Sun Choi* Abstract Background: A reservoir of HIV-1 is a major obstacle in eliminating HIV-1 in patients because it can reactivate in stopping antiretroviral therapy (ART). Histone modifications, such as acetylation and methylation, play a critical role in the organization of chromatin domains and the up- or downregulation of gene expression. Although many studies have reported that an epigenetic mechanism is strongly involved in the maintenance of HIV-1 transcriptional latency, neither the epigenetic control of viral replication nor how HIV-1 latency is maintained is not fully understood. Results: We re-analyzed a high throughput parallel DNA sequencing (ChIP-seq) data from previous work to investigate the effect of histone modifications, H3K4me3 and H3K9ac, on HIV-1 latency in terms of chromosome distribution. The outputs of ChIP-seq from uninfected CD4+ T cell lines and HIV-1 latently infected cells were aligned to hg18 using bowtie and then analyzed using various software packages. Certain chromosomes (16, 17, 19, and 22) were significantly enriched for histone modifications in both decreased and increased islands. In the same chromosomes in HIV-1 latently infected cells, 38 decreased and 41 increased islands from common islands of H3K4me3 and H3K9ac were selected for functional annotation. In Gene Ontology analysis, the 38 genes associated with decreased islands were involved in the regulation of biological process, regulation of cellular process, biological regulation, and purinergic receptor signaling pathway, while the 41 genes associated with increased islands were involved in nucleic acid binding, calcium-activated cation channel activity, DNA binding, and zinc ion binding.
    [Show full text]