DOCSLIB.ORG

Mouse Arv1 Conditional Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mouse Arv1 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Arv1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Arv1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Arv1 gene (NCBI Reference Sequence: NM_026855 ; Ensembl: ENSMUSG00000031982 ) is located on Mouse chromosome 8. 6 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 5 (Transcript: ENSMUST00000034463). Exon 3 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Arv1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-296L15 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: Homozygous KO causes female infertility, reduction in body weight, amount of white adipose tissue and plasma lipid levels and increase in adiponectin levels, food consumption, energy expenditure and activity levels. Exon 3 starts from about 35.09% of the coding region. The knockout of Exon 3 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 2953 bp, and the size of intron 3 for 3'-loxP site insertion: 2309 bp. The size of effective cKO region: ~654 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 3 6 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Arv1 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(7154bp) | A(25.27% 1808) | C(22.24% 1591) | T(29.0% 2075) | G(23.48% 1680) 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% chr8 + 124725080 124728079 3000 browser details YourSeq 257 740 1036 3000 95.2% chr4 - 116790820 116791144 325 browser details YourSeq 257 728 1035 3000 95.8% chr10 + 4258685 4259291 607 browser details YourSeq 253 931 1690 3000 80.7% chr4 + 135290120 135290541 422 browser details YourSeq 244 739 1033 3000 95.2% chr4 + 59182670 59182968 299 browser details YourSeq 230 743 1035 3000 93.3% chr12 - 111197843 111198143 301 browser details YourSeq 210 729 1422 3000 88.8% chr11 - 97308250 97308707 458 browser details YourSeq 195 723 1037 3000 94.5% chr7 + 112860686 112861224 539 browser details YourSeq 187 809 1035 3000 93.9% chr13 + 3714752 3714997 246 browser details YourSeq 186 728 1036 3000 88.8% chr5 - 110631073 110631289 217 browser details YourSeq 185 728 1037 3000 88.3% chr19 + 8890159 8890365 207 browser details YourSeq 184 837 1037 3000 94.4% chr5 - 34522535 34522731 197 browser details YourSeq 183 843 1051 3000 95.6% chr10 - 5113710 5113919 210 browser details YourSeq 183 737 1036 3000 88.2% chr7 + 35538335 35538543 209 browser details YourSeq 182 837 1036 3000 95.5% chr2 - 156171484 156171683 200 browser details YourSeq 182 840 1037 3000 96.5% chr2 + 119129131 119129329 199 browser details YourSeq 179 842 1037 3000 96.5% chr2 - 128617201 128617443 243 browser details YourSeq 178 840 1037 3000 95.0% chr8 - 68058162 68058359 198 browser details YourSeq 178 836 1039 3000 95.5% chr3 - 88273537 88518871 245335 browser details YourSeq 178 732 1037 3000 89.3% chr2 - 125846516 125846764 249 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% chr8 + 124728734 124731733 3000 browser details YourSeq 136 144 1572 3000 87.1% chr5 - 127224689 127709149 484461 browser details YourSeq 130 1488 1638 3000 95.9% chr12 + 95922536 95922699 164 browser details YourSeq 125 1491 1637 3000 93.8% chr6 - 113646128 113646275 148 browser details YourSeq 124 1491 1637 3000 96.4% chr13 - 58613454 58613985 532 browser details YourSeq 123 1491 1637 3000 90.8% chr14 - 51933790 51933932 143 browser details YourSeq 123 1502 1729 3000 91.9% chr10 + 63087754 63088350 597 browser details YourSeq 121 1491 1636 3000 89.4% chr2 + 104300845 104300976 132 browser details YourSeq 120 1406 1638 3000 84.4% chr4 + 136209855 136210011 157 browser details YourSeq 117 1494 1841 3000 84.5% chr11 + 20179696 20179817 122 browser details YourSeq 116 3 256 3000 86.0% chr5 - 126105730 126106062 333 browser details YourSeq 113 1491 1632 3000 92.7% chr1 - 84611164 84611316 153 browser details YourSeq 111 8 261 3000 87.3% chr12 + 80487273 80487534 262 browser details YourSeq 109 16 256 3000 86.0% chr15 + 35188744 35189051 308 browser details YourSeq 108 1491 1637 3000 89.4% chr17 + 31700896 31701041 146 browser details YourSeq 107 20 257 3000 86.3% chrX - 95674839 95675100 262 browser details YourSeq 107 1489 1636 3000 92.8% chr12 + 99598458 99598901 444 browser details YourSeq 104 8 255 3000 79.2% chr9 + 32001732 32001946 215 browser details YourSeq 103 1491 1618 3000 94.1% chr2 + 30704052 30704180 129 browser details YourSeq 102 1489 1608 3000 95.6% chr10 - 78301383 78301651 269 Note: The 3000 bp section downstream of Exon 3 is BLAT searched against the genome. No significant similarity is found. Page 4 of 7 https://www.alphaknockout.com Gene and protein information: Arv1 ARV1 homolog, fatty acid homeostasis modulator [ Mus musculus (house mouse) ] Gene ID: 68865, updated on 24-Oct-2019 Gene summary Official Symbol Arv1 provided by MGI Official Full Name ARV1 homolog, fatty acid homeostasis modulator provided by MGI Primary source MGI:MGI:1916115 See related Ensembl:ENSMUSG00000031982 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 AI461928; AW121084; 1110067L22Rik Expression Ubiquitous expression in thymus adult (RPKM 10.0), ovary adult (RPKM 7.3) and 28 other tissues See more Orthologs human all Genomic context Location: 8; 8 E2 See Arv1 in Genome Data Viewer Exon count: 7 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 8 NC_000074.6 (124722026..124742794) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 8 NC_000074.5 (127246039..127258023) Chromosome 8 - NC_000074.6 Page 5 of 7 https://www.alphaknockout.com Transcript information: This gene has 3 transcripts Gene: Arv1 ENSMUSG00000031982 Description ARV1 homolog, fatty acid homeostasis modulator [Source:MGI Symbol;Acc:MGI:1916115] Gene Synonyms 1110067L22Rik Location Chromosome 8: 124,722,139-124,734,123 forward strand. GRCm38:CM001001.2 About this gene This gene has 3 transcripts (splice variants), 195 orthologues, is a member of 1 Ensembl protein family and is associated with 27 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Arv1- ENSMUST00000034463.3 1165 266aa ENSMUSP00000034463.3 Protein coding CCDS22774 Q0VBH5 TSL:1 201 Q9D0U9 GENCODE basic APPRIS P1 Arv1- ENSMUST00000212959.1 937 149aa ENSMUSP00000148335.1 Protein coding - A0A1D5RLE6 CDS 5' 203 incomplete TSL:1 Arv1- ENSMUST00000212036.1 891 112aa ENSMUSP00000148475.1 Nonsense mediated - A0A1D5RLR4 TSL:1 202 decay 31.98 kb Forward strand 124.72Mb 124.73Mb 124.74Mb Genes (Comprehensive set... Arv1-201 >protein coding Arv1-202 >nonsense mediated decay Arv1-203 >protein coding Contigs < AC151736.6 Genes < Ttc13-212protein coding < Fam89a-201protein coding (Comprehensive set... < Ttc13-202protein coding < Ttc13-210protein coding < Ttc13-203protein coding < Ttc13-201protein coding < Ttc13-205retained intron Regulatory Build 124.72Mb 124.73Mb 124.74Mb Reverse strand 31.98 kb Regulation Legend CTCF Open Chromatin Promoter Promoter Flank Gene Legend Protein Coding merged Ensembl/Havana Ensembl protein coding Non-Protein Coding processed transcript Page 6 of 7 https://www.alphaknockout.com Transcript: ENSMUST00000034463 11.98 kb Forward strand Arv1-201 >protein coding ENSMUSP00000034... Transmembrane heli... Low complexity (Seg) Pfam Arv1 protein PANTHER PTHR14467:SF0 Arv1 protein All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant synonymous variant Scale bar 0 40 80 120 160 200 266 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 7 of 7.
Load more

Recommended publications
  • 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]
  • 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]
  • RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA- 3 Seq Data
    bioRxiv preprint doi: https://doi.org/10.1101/2020.11.06.371724; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 1 Title 2 NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA- 3 Seq Data 4 Authors/Affiliations 5 Eliah G. Overbey⇞,1, Amanda M. Saravia-Butler⇞,2,3, Zhe Zhang4, Komal S. Rathi4, Homer 6 Fogle5,3, Willian A. da Silveira6, Richard J. Barker7, Joseph J. Bass8, Afshin Beheshti37,38, Daniel 7 C. Berrios39, Elizabeth A. Blaber9, Egle Cekanaviciute3, Helio A. Costa10, Laurence B. Davin11, 8 Kathleen M. Fisch12, SamraWit G. Gebre3,37, MattheW Geniza13, Rachel Gilbert14, Simon Gilroy7, 9 Gary Hardiman6,15, Raúl Herranz16, Yared H. Kidane17, Colin P.S. Kruse18, Michael D. Lee19,20, 10 Ted Liefeld21, Norman G. LeWis11, J. Tyson McDonald22, Robert Meller23, TejasWini Mishra24, 11 Imara Y. Perera25, Shayoni Ray26, Sigrid S. Reinsch3, Sara Brin Rosenthal12, Michael Strong27, 12 Nathaniel J SzeWczyk28, Candice G.T. Tahimic29, Deanne M. Taylor30, Joshua P. Vandenbrink31, 13 Alicia Villacampa16, Silvio Weging32, Chris Wolverton33, Sarah E. Wyatt34,35, Luis Zea36, 14 Sylvain V. Costes*,3, Jonathan M. Galazka*,3 15 1: Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA 16 2: Logyx, LLC, Mountain VieW, CA 94043, USA 17 3: Space Biosciences Division, NASA
    [Show full text]
  • Supplementary Figures S1-3, Tables S1-3
    Supplementary Material Supplementary Material and Methods MEFs preparation and electroporation Dissected embryos were washed in cold PBS, head and entrails (heart, liver) were excised and used for genotyping. The remaining embryonic tissues were sliced, incubated in Trypsin/EDTA solution (15 min at 37°C; Sigma), further dissociated and cultured in Iscove's Modified Dulbecco's Medium [(IMDM); purchased from Sigma)] supplemented with 10% fetal bovine serum (FBS), penicillin/streptomycin (100 UI), 2 mM L-glutamine and 1× non-essential amino acids (all chemicals were from Gibco). Cells were electroporated in buffer (125 mM KCl, 15 mM NaCl, 3 mM glucose, 25 mM Hepes pH 7.4, 1.2 mM MgCl2) supplemented with 0.66 % (w/v) ethylene glycol (PEG) 4000 in 4 mm gap cuvettes using an ECM 830 electroporator (BTX; settings: 240V, 30 ms). Luciferase assay and NF-kB pathway activation The luciferase assay was performed according to the supplier’s protocol using Dual-Glo Luciferase Assay System (Promega). To activate the NF-ĸB pathway, cells were stimulated overnight with 20 ng/ml TNFα (stock 100 µg/ml; Peprotech), 20 ng/ml lymphotoxin (LTα; stock 10 µg/ml; Invivogen) or 100 ng/ml Pam3csk4 (stock 100 µg/ml, Invivogen). PCR primers used for ddPCR The first primer is in forward and the second primer in reverse orientation: β-ACTIN, 5’- CTAGGCGGACTATGAC-3’, 5’- GACTTGGGAGAGGACT-3’; SIRT1, 5’- CTCCAGGCAGATGCCATAAC-3’, 5’- TGCCCTTGGTTAAAATTTGG-3’; TLR2, 5’- CCTCCAATCAGGCTTCTCTG-3’, 5’- TGGAGGTTCACACACCTCTG-3’. Antibodies used for IHC and Western blotting IHC: anti-β-catenin (mouse monoclonal, EM-22, Exbio), anti-chromogranin A (rabbit polyclonal, ab15160, Abcam), anti-lysozyme (rabbit polyclonal, A0099, Dako), anti-p21 (mouse monoclonal, SXM30, BD Pharmingen), anti-p53 (rabbit polyclonal, NCL-p53-CM5p, Novocastra), anti-PCNA (rabbit polyclonal, ab18197, Abcam), anti-Tlr2 (goat polyclonal, sc-16237, Santa Cruz).
    [Show full text]
  • Supplementary Figure 1. Dystrophic Mice Show Unbalanced Stem Cell Niche
    Supplementary Figure 1. Dystrophic mice show unbalanced stem cell niche. (A) Single channel images for the merged panels shown in Figure 1A, for of PAX7, MYOD and Laminin immunohistochemical staining in Lmna Δ8-11 mice of PAX7 and MYOD markers at the indicated days of post-natal growth. Basement membrane of muscle fibers was stained with Laminin. Scale bars, 50 µm. (B) Quantification of the % of PAX7+ MuSCs per 100 fibers at the indicated days of post-natal growth in (A). n =3-6 animals per genotype. (C) Immunohistochemical staining in Lmna Δ8-11 mice of activated, ASCs (PAX7+/KI67+) and quiescent QSCs (PAX7+/Ki67-) MuSCs at d19 and relative quantification (below). n= 4-6 animals per genotype. Scale bars, 50 µm. (D) Quantification of the number of cells per cluster in single myofibers extracted from d19 Lmna Δ8-11 mice and cultured 96h. n= 4-5 animals per group. Data are box with median and whiskers min to max. B, C, Data are mean ± s.e.m. Statistics by one-way (B) or two-way (C, D) analysis of variance (ANOVA) with multiple comparisons. * * P < 0.01, * * * P < 0.001. wt= Lmna Δ8-11 +/+; het= Lmna Δ8-11 +/; hom= Lmna Δ8-11 -/-. Supplementary Figure 2. Heterozygous mice show intermediate Lamin A levels. (A) RNA-seq signal tracks as the effective genome size normalized coverage of each biological replicate of Lmna Δ8-11 mice on Lmna locus. Neomycine cassette is indicated as a dark blue rectangle. (B) Western blot of total protein extracted from the whole Lmna Δ8-11 muscles at d19 hybridized with indicated antibodies.
    [Show full text]
  • Phenotypic Implications of Genetic Interaction Networks in Saccharomyces Cerevisiae Bede Phillip Busby
    Phenotypic implications of genetic interaction networks in Saccharomyces cerevisiae By Bede Phillip Busby A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy In Cell and Molecular Bioscience Victoria University of Wellington 2015 Abstract Gene functions were studied as extensive networks comprising synergistic functional interactions between overlapping pairs of genes. Elucidation of such networks related to drug phenotypes (statins in this thesis) provides additional information to classical genetics as to what genes and metabolic pathways might be involved in phenotypes and, importantly, where side-effects might arise in drug effects. A key question is whether there are genetic interaction networks that vary with individuals and with phenotypes. To answer this question a panel of twenty-six fully sequenced yeast strains from the Saccharomyces Genome Resequencing Project (SGRP; Sanger Institute) was screened for statin resistance to approximate a model for individuals in a human population. Three strains (Y55, SK1 and YPS606) were shown to be 500% more resistant to atorvastatin than the S288C laboratory control strain and were selected for further analysis. Synthetic genetic array analysis (SGA) and chemical genetic profiling were utilised to elucidate genetic interaction networks in the four different strains. SGA analysis depends on the availability of a genome-wide deletion mutant array (DMA) which already exists for S288C and the current studies constructed equivalent Y55-, SK1-, and YS606-strain specific deletion mutant arrays called here “ssDMA’s”. Creating the new ssDMAs involved six back-crossings (1-1/26) of Y55, SK1 and YPS606 with S288C to place the genome-wide deletion mutations of S288C on the genetic background of the strains using appropriate selection markers between each backcrossing.
    [Show full text]
  • Molecular Determinants of the Cytotoxicity of Platinum Compounds: the Contribution of in Silico Research
    [CANCER RESEARCH 64, 356–362, January 1, 2004] Molecular Determinants of the Cytotoxicity of Platinum Compounds: The Contribution of in Silico Research Antoine Vekris,1 Delphine Meynard,2 Marie-Christine Haaz,1 Martine Bayssas,3 Jacques Bonnet,2 and Jacques Robert2 1DiGEM S. A., Bordeaux, France; 2Laboratory of Pharmacology of Anticancer Agents, CNRS FRE 2618 Institut Bergonie´and Universite´Victor Segalen Bordeaux 2, Bordeaux, France; and 3Debiopharm S. A., Lausanne, Switzerland ABSTRACT compounds presenting no cross-resistance with the original com- pound. Several hundreds of cisplatin analogs have been synthesized Gene expression profiling of tumors allows the establishment of rela- and screened in various systems, but only very few of them have tionships between gene expression profiles and sensitivity to anticancer shown a clinical interest deserving their introduction in the anticancer drugs. In an attempt to study the molecular determinants of the activity of platinum compounds, we explored the publicly available databases of the armamentarium (4). National Cancer Institute (NCI; http://dtp.nci.nih.gov), which allow access Carboplatin (see structure in Fig. 1), the first analog to be approved to the gene expression profiles of the 60 cell lines for which drug cytotox- for cancer chemotherapy, is now widely used in ovarian and testicular icity patterns already existed. Using this database, we have conducted an cancer as well as in the other indications of cisplatin. In contrast, the in silico research to identify the genes the expression of which was posi- toxicity profile of carboplatin is quite different from that of cisplatin, tively or negatively correlated to the sensitivity to four platinum com- with myelosuppression as the major dose-limiting toxicity.
    [Show full text]
  • Investigating Developmental and Epileptic Encephalopathy Using Drosophila Melanogaster
    International Journal of Molecular Sciences Review Investigating Developmental and Epileptic Encephalopathy Using Drosophila melanogaster Akari Takai 1 , Masamitsu Yamaguchi 2,3, Hideki Yoshida 2 and Tomohiro Chiyonobu 1,* 1 Department of Pediatrics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan; [email protected] 2 Department of Applied Biology, Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto 603-8585, Japan; [email protected] (M.Y.); [email protected] (H.Y.) 3 Kansai Gakken Laboratory, Kankyo Eisei Yakuhin Co. Ltd., Kyoto 619-0237, Japan * Correspondence: [email protected] Received: 15 August 2020; Accepted: 1 September 2020; Published: 3 September 2020 Abstract: Developmental and epileptic encephalopathies (DEEs) are the spectrum of severe epilepsies characterized by early-onset, refractory seizures occurring in the context of developmental regression or plateauing. Early infantile epileptic encephalopathy (EIEE) is one of the earliest forms of DEE, manifesting as frequent epileptic spasms and characteristic electroencephalogram findings in early infancy. In recent years, next-generation sequencing approaches have identified a number of monogenic determinants underlying DEE. In the case of EIEE, 85 genes have been registered in Online Mendelian Inheritance in Man as causative genes. Model organisms are indispensable tools for understanding the in vivo roles of the newly identified causative genes. In this review, we first present an overview of epilepsy and its genetic etiology, especially focusing on EIEE and then briefly summarize epilepsy research using animal and patient-derived induced pluripotent stem cell (iPSC) models. The Drosophila model, which is characterized by easy gene manipulation, a short generation time, low cost and fewer ethical restrictions when designing experiments, is optimal for understanding the genetics of DEE.
    [Show full text]
  • Milger Et Al. Pulmonary CCR2+CD4+ T Cells Are Immune Regulatory And
    Milger et al. Pulmonary CCR2+CD4+ T cells are immune regulatory and attenuate lung fibrosis development Supplemental Table S1 List of significantly regulated mRNAs between CCR2+ and CCR2- CD4+ Tcells on Affymetrix Mouse Gene ST 1.0 array. Genewise testing for differential expression by limma t-test and Benjamini-Hochberg multiple testing correction (FDR < 10%). Ratio, significant FDR<10% Probeset Gene symbol or ID Gene Title Entrez rawp BH (1680) 10590631 Ccr2 chemokine (C-C motif) receptor 2 12772 3.27E-09 1.33E-05 9.72 10547590 Klrg1 killer cell lectin-like receptor subfamily G, member 1 50928 1.17E-07 1.23E-04 6.57 10450154 H2-Aa histocompatibility 2, class II antigen A, alpha 14960 2.83E-07 1.71E-04 6.31 10590628 Ccr3 chemokine (C-C motif) receptor 3 12771 1.46E-07 1.30E-04 5.93 10519983 Fgl2 fibrinogen-like protein 2 14190 9.18E-08 1.09E-04 5.49 10349603 Il10 interleukin 10 16153 7.67E-06 1.29E-03 5.28 10590635 Ccr5 chemokine (C-C motif) receptor 5 /// chemokine (C-C motif) receptor 2 12774 5.64E-08 7.64E-05 5.02 10598013 Ccr5 chemokine (C-C motif) receptor 5 /// chemokine (C-C motif) receptor 2 12774 5.64E-08 7.64E-05 5.02 10475517 AA467197 expressed sequence AA467197 /// microRNA 147 433470 7.32E-04 2.68E-02 4.96 10503098 Lyn Yamaguchi sarcoma viral (v-yes-1) oncogene homolog 17096 3.98E-08 6.65E-05 4.89 10345791 Il1rl1 interleukin 1 receptor-like 1 17082 6.25E-08 8.08E-05 4.78 10580077 Rln3 relaxin 3 212108 7.77E-04 2.81E-02 4.77 10523156 Cxcl2 chemokine (C-X-C motif) ligand 2 20310 6.00E-04 2.35E-02 4.55 10456005 Cd74 CD74 antigen
    [Show full text]
  • Investigation of Genetically-Regulated Gene Expression and Response to Treatment in Rheumatoid Arthritis Highlights an Associati
    1 Investigation of genetically-regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response Svetlana Cherlin1, Myles J. Lewis2, Darren Plant3,4, Nisha Nair3, Katriona Goldmann2, Evan Tzanis2, Michael R. Barnes2, Paul McKeigue5, Jennifer H. Barrett6,7, Costantino Pitzalis2, Anne 3,4 1∗ Barton , MATURA Consortium, Heather J. Cordell 1 Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK 2 Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK 3 Centre of Genetics & Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, UK 4 NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust Manchester Academic Health Science Centre, Manchester, UK 5 Centre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK 6 NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK 7 School of Medicine, University of Leeds, Leeds, UK Corresponding author: Heather J. Cordell Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK Tel: +44 (0)191 241 8669 E-mail: [email protected] Running title: Association between IL18RAP expression and treatment response in RA 2 ABSTRACT Objectives In this study, we sought to investigate whether there was any association between genetically-regulated gene expression (as predicted using various reference panels) and anti-TNF treatment response (change in erythrocyte sedimentation rate, ESR) using 3,158 European ancestry rheumatoid arthritis patients.
    [Show full text]
  • Impaired Retrograde Transport by the Dynein/Dynactin Complex Contributes to Tau-Induced Toxicity Malte Butzlaff1,2,†, Shabab B
    Human Molecular Genetics, 2015, Vol. 24, No. 13 3623–3637 doi: 10.1093/hmg/ddv107 Advance Access Publication Date: 20 March 2015 Original Article ORIGINAL ARTICLE Impaired retrograde transport by the Dynein/Dynactin complex contributes to Tau-induced toxicity Malte Butzlaff1,2,†, Shabab B. Hannan3,†, Peter Karsten1, Sarah Lenz1, Josephine Ng3, Hannes Voßfeldt1, Katja Prüßing1, Ralf Pflanz4, Jörg B. Schulz1,5,‡, Tobias Rasse3,6,‡ and Aaron Voigt1,‡,* 1Department of Neurology, University Hospital, RWTH Aachen, Germany, 2Department of Cellular Neurophysiology, Hannover Medical School, Hannover, Germany, 3Department of Synaptic Plasticity, Hertie- Institute for Clinical Brain Research, University of Tübingen, Germany, 4Department of Molecular Development Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany, 5JARA – Brain Translational Medicine, Aachen, Germany and 6Schaller Research Group, University of Heidelberg, Deutsches Krebsforschungszentrum (DKFZ), Proteostasis in Neurodegenerative Disease (B180), Heidelberg, Germany *To whom correspondence should be addressed at: Department of Neurology, University Hospital, RWTH Aachen, Pauwelsstrasse 30, D-52074 Aachen, Germany. Tel: +49 2418085054; Fax: +49 2418082582; Email: [email protected] Abstract The gene mapt codes for the microtubule-associated protein Tau. The R406W amino acid substitution in Tau is associated with frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) characterized by Tau-positive filamentous inclusions. These filamentous Tau inclusions are present in a group of neurodegenerative diseases known as tauopathies, including Alzheimer’s disease (AD). To gain more insights into the pathomechanism of tauopathies, we performed an RNAi-based large-scale screen in Drosophila melanogaster to identify genetic modifiers of Tau[R406W]-induced toxicity. A collection of RNAi lines, putatively silencing more than 7000 genes, was screened for the ability to modify Tau[R406W]-induced toxicity in vivo.
    [Show full text]
  • Pan-Cancer Molecular Subtypes Revealed by Mass-Spectrometry
    ARTICLE https://doi.org/10.1038/s41467-019-13528-0 OPEN Pan-cancer molecular subtypes revealed by mass- spectrometry-based proteomic characterization of more than 500 human cancers Fengju Chen1, Darshan S. Chandrashekar2,3, Sooryanarayana Varambally2,3,4 & Chad J. Creighton 1,5,6,7* Mass-spectrometry-based proteomic profiling of human cancers has the potential for pan- cancer analyses to identify molecular subtypes and associated pathway features that might 1234567890():,; be otherwise missed using transcriptomics. Here, we classify 532 cancers, representing six tissue-based types (breast, colon, ovarian, renal, uterine), into ten proteome-based, pan- cancer subtypes that cut across tumor lineages. The proteome-based subtypes are obser- vable in external cancer proteomic datasets surveyed. Gene signatures of oncogenic or metabolic pathways can further distinguish between the subtypes. Two distinct subtypes both involve the immune system, one associated with the adaptive immune response and T-cell activation, and the other associated with the humoral immune response. Two addi- tional subtypes each involve the tumor stroma, one of these including the collagen VI interacting network. Three additional proteome-based subtypes—respectively involving proteins related to Golgi apparatus, hemoglobin complex, and endoplasmic reticulum—were not reflected in previous transcriptomics analyses. A data portal is available at UALCAN website. 1 Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA. 2 Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA. 3 Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA. 4 The Informatics Institute, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
    [Show full text]