DOCSLIB.ORG

A High Throughput, Functional Screen of Human Body Mass Index GWAS Loci Using Tissue-Specific Rnai Drosophila Melanogaster Crosses Thomas J

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A High Throughput, Functional Screen of Human Body Mass Index GWAS Loci Using Tissue-Specific Rnai Drosophila Melanogaster Crosses Thomas J Washington University School of Medicine Digital Commons@Becker Open Access Publications 2018 A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses Thomas J. Baranski Washington University School of Medicine in St. Louis Aldi T. Kraja Washington University School of Medicine in St. Louis Jill L. Fink Washington University School of Medicine in St. Louis Mary Feitosa Washington University School of Medicine in St. Louis Petra A. Lenzini Washington University School of Medicine in St. Louis See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Recommended Citation Baranski, Thomas J.; Kraja, Aldi T.; Fink, Jill L.; Feitosa, Mary; Lenzini, Petra A.; Borecki, Ingrid B.; Liu, Ching-Ti; Cupples, L. Adrienne; North, Kari E.; and Province, Michael A., ,"A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses." PLoS Genetics.14,4. e1007222. (2018). https://digitalcommons.wustl.edu/open_access_pubs/6820 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Thomas J. Baranski, Aldi T. Kraja, Jill L. Fink, Mary Feitosa, Petra A. Lenzini, Ingrid B. Borecki, Ching-Ti Liu, L. Adrienne Cupples, Kari E. North, and Michael A. Province This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/6820 RESEARCH ARTICLE A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses Thomas J. Baranski1*, Aldi T. Kraja2, Jill L. Fink1, Mary Feitosa2, Petra A. Lenzini2, Ingrid B. Borecki3, Ching-Ti Liu4, L. Adrienne Cupples4, Kari E. North5, Michael A. Province2* a1111111111 1 Department of Internal Medicine, Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St. Louis, Missouri, United States of America, 2 Department of Genetics and a1111111111 Center for Genome Sciences and Systems Biology, Division of Statistical Genomics, Washington University a1111111111 School of Medicine, St. Louis, Missouri, United States of America, 3 Department of Biostatistics, University of a1111111111 Washington, Seattle, Washington, United States of America, 4 Department of Biostatistics, Boston University a1111111111 School of Public Health, Boston, Massachusetts, United States of America, 5 Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America * [email protected] (TJB); [email protected] (MAP) OPEN ACCESS Citation: Baranski TJ, Kraja AT, Fink JL, Feitosa M, Abstract Lenzini PA, Borecki IB, et al. (2018) A high Human GWAS of obesity have been successful in identifying loci associated with adiposity, throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi but for the most part, these are non-coding SNPs whose function, or even whose gene of Drosophila melanogaster crosses. PLoS Genet 14 action, is unknown. To help identify the genes on which these human BMI loci may be oper- (4): e1007222. https://doi.org/10.1371/journal. ating, we conducted a high throughput screen in Drosophila melanogaster. Starting with 78 pgen.1007222 BMI loci from two recently published GWAS meta-analyses, we identified fly orthologs of all Editor: Giovanni Bosco, Geisel School of Medicine nearby genes (± 250KB). We crossed RNAi knockdown lines of each gene with flies contain- at Dartmouth, UNITED STATES ing tissue-specific drivers to knock down (KD) the expression of the genes only in the brain Received: September 22, 2017 and the fat body. We then raised the flies on a control diet and compared the amount of fat/ Accepted: January 25, 2018 triglyceride in the tissue-specific KD group compared to the driver-only control flies. 16 of Published: April 2, 2018 the 78 BMI GWAS loci could not be screened with this approach, as no gene in the 500-kb region had a fly ortholog. Of the remaining 62 GWAS loci testable in the fly, we found a signifi- Copyright: © 2018 Baranski et al. This is an open access article distributed under the terms of the cant fat phenotype in the KD flies for at least one gene for 26 loci (42%) even after correcting Creative Commons Attribution License, which for multiple comparisons. By contrast, the rate of significant fat phenotypes in RNAi KD found permits unrestricted use, distribution, and in a recent genome-wide Drosophila screen (Pospisilik et al. (2010) is ~5%. More interest- reproduction in any medium, provided the original author and source are credited. ingly, for 10 of the 26 positive regions, we found that the nearest gene was not the one that showed a significant phenotype in the fly. Specifically, our screen suggests that for the 10 Data Availability Statement: All relevant data are within the paper and its Supporting Information human BMI SNPs rs11057405, rs205262, rs9925964, rs9914578, rs2287019, rs11688816, files. rs13107325, rs7164727, rs17724992, and rs299412, the functional genes may NOT be the Funding: This work was supported in part by nearest ones (CLIP1, C6orf106, KAT8, SMG6, QPCTL, EHBP1, SLC39A8, ADPGK National Institute of Diabetes and Digestive and /ADPGK-AS1, PGPEP1, KCTD15, respectively), but instead, the specific nearby cis genes Kidney Diseases (NIDDK) grant DK089256 are the functional target (namely: ZCCHC8, VPS33A, RSRC2; SPDEF, NUDT3; PAGR1; (Multiple PIs MAP, TJB, CTL, LAC, KEN and IBB). SETD1, VKORC1; SGSM2, SRR; VASP, SIX5; OTX1; BANK1; ARIH1; ELL; CHST8, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007222 April 2, 2018 1 / 18 Fly functional RNAi screen for BMI Competing interests: The authors have declared respectively). The study also suggests further functional experiments to elucidate mecha- that no competing interests exist. nism of action for genes evolutionarily conserved for fat storage. Author summary Human Genome Wide Association Studies have successfully found thousands of novel genetic variants associated with many diseases. While these undoubtedly point to new biology, the field has been slowed in exploiting these new findings to reach a better under- standing of exactly how they confer increased risk. Many, if not most, appear to be regula- tory not coding variants, so their immediate consequence is not obvious. A real rate limiting step is even identifying which gene these variants might be regulating, and in what tissues they are operating to increase disease risk. In the absence of any other infor- mation, a first order assumption is that they may be more likely to be regulating a nearby gene, and such variants are often initially annotated by the ªnearestº gene until their func- tion is more definitively validated. Exploiting the idea that many genes may have con- served function across species, we conducted a high-throughput screen of fruit-fly orthologs of human genes nearby 78 well validated GWAS variants for human obesity, in order to more precisely identify the gene(s) of action. We systematically knocked down the function of each of these nearby genes in the brain and fat-body of the flies, raised them on a standard diet, and compared their percent body fat with control flies, in order to validate which genes showed a fat response. 43% of the time when fly orthologs existed in the region, we were able to identify the causal gene. Interestingly, nearly half the time (46%), it was not the nearest gene but another nearby one that regulated fat. Introduction Human Genome-Wide Association Scans (GWAS) have been successful in discovering many genetic loci that are significantly associated with Body Mass Index (BMI). These associations have been replicated across consortia consisting of many large and independent studies, some- times numbering into hundreds of thousands of subjects. From a statistical point of view, the evidence that single nucleotide polymorphisms (SNPs) are tagging something real is over- whelming. However, progress has been slowed in moving beyond the discovery phase to a deeper understanding the biological significance of these findings, due to difficulties isolating the driving causal variants or even identifying the acting genes tagged by these GWAS variants. Most of the findings are not in gene coding regions. Indeed, many are intergenic, suggesting that much of the underlying modes of action of these loci may be regulatory. Unfortunately, our limited biological understanding of the regulome has hampered further progress. While the field has begun serious annotation of the regulatory regions of the genome with ini- tiatives and resources such as ENCODE, RoadMap and GTEx, the annotation is still far from complete and the answers that are emerging are complex. As a result, many publications anno- tate the statistically significant SNPs simply with the ªclosestº gene, even though trans-acting reg- ulatory sequences certainly exist[1], and some enhancers have been shown to regulate multiple genes[2]. Recently, it was reported that rs1421085 T-to-C intron of the well-known obesity-asso- ciated FTO gene, disrupts a conserved motif for the ARID5B repressor,
Load more

Recommended publications
  • Deregulated Gene Expression Pathways in Myelodysplastic Syndrome Hematopoietic Stem Cells
    Leukemia (2010) 24, 756–764 & 2010 Macmillan Publishers Limited All rights reserved 0887-6924/10 $32.00 www.nature.com/leu ORIGINAL ARTICLE Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells A Pellagatti1, M Cazzola2, A Giagounidis3, J Perry1, L Malcovati2, MG Della Porta2,MJa¨dersten4, S Killick5, A Verma6, CJ Norbury7, E Hellstro¨m-Lindberg4, JS Wainscoat1 and J Boultwood1 1LRF Molecular Haematology Unit, NDCLS, John Radcliffe Hospital, Oxford, UK; 2Department of Hematology Oncology, University of Pavia Medical School, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; 3Medizinische Klinik II, St Johannes Hospital, Duisburg, Germany; 4Division of Hematology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; 5Department of Haematology, Royal Bournemouth Hospital, Bournemouth, UK; 6Albert Einstein College of Medicine, Bronx, NY, USA and 7Sir William Dunn School of Pathology, University of Oxford, Oxford, UK To gain insight into the molecular pathogenesis of the the World Health Organization.6,7 Patients with refractory myelodysplastic syndromes (MDS), we performed global gene anemia (RA) with or without ringed sideroblasts, according to expression profiling and pathway analysis on the hemato- poietic stem cells (HSC) of 183 MDS patients as compared with the the French–American–British classification, were subdivided HSC of 17 healthy controls. The most significantly deregulated based on the presence or absence of multilineage dysplasia. In pathways in MDS include interferon signaling, thrombopoietin addition, patients with RA with excess blasts (RAEB) were signaling and the Wnt pathways. Among the most signifi- subdivided into two categories, RAEB1 and RAEB2, based on the cantly deregulated gene pathways in early MDS are immuno- percentage of bone marrow blasts.
    [Show full text]
  • Seq2pathway Vignette
    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
    [Show full text]
  • Genome-Wide Identification and Analysis of Prognostic Features in Human Cancers
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.01.446243; this version posted June 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 4.0 International license. Genome-wide identification and analysis of prognostic features in human cancers Joan C. Smith1,2 and Jason M. Sheltzer1* 1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724. 2. Google, Inc., New York, NY 10011. * Lead contact; to whom correspondence may be addressed. E-mail: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2021.06.01.446243; this version posted June 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 4.0 International license. Abstract Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to accurately identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients with known clinical outcomes. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically-ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets.
    [Show full text]
  • Despite Differential Gene Expression Profiles Pediatric MDS Derived Mesenchymal Stromal Cells Display Functionality in Vitro☆ F.G.J
    Stem Cell Research (2015) 14, 198-210 Available online at www.sciencedirect.com ScienceDirect www.elsevier.com/locate/scr Despite differential gene expression profiles pediatric MDS derived mesenchymal stromal cells display functionality in vitro☆ F.G.J. Calkoen a,⁎, C. Vervat a, M. van Pel b, V. de Haas c, L.S. Vijfhuizen d, E. Eising d, W.G.M. Kroes e, P.A.C. 't Hoen d, M.M. van den Heuvel-Eibrink c,f, R.M. Egeler a,g, M.J.D. van Tol a, L.M. Ball a a Department of Pediatrics, Section Immunology, Hematology/Oncology and Hematopoietic Stem Cell Transplantation, Leiden University Medical Center, Leiden, The Netherlands b Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands c Dutch Childhood Oncology Group (DCOG), The Hague, The Netherlands d Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands e Laboratory for Diagnostic Genome Analysis, Leiden University Medical Center, Leiden, The Netherlands f Department of Pediatric Oncology/Hematology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands g Department of Hematology/Oncology and Hematopoietic Stem Cell Transplantation, Hospital for Sick Children, University of Toronto, Toronto, Canada Received 24 June 2014; received in revised form 3 December 2014; accepted 19 January 2015 Available online 28 January 2015 Abstract Pediatric myelodysplastic syndrome (MDS) is a heterogeneous disease covering a spectrum ranging from aplasia (RCC) to myeloproliferation (RAEB(t)). In adult-type MDS there is increasing evidence for abnormal function of the bone-marrow microenvironment. Here, we extensively studied the mesenchymal stromal cells (MSCs) derived from children with MDS.
    [Show full text]
  • ARTICLE Doi:10.1038/Nature10523
    ARTICLE doi:10.1038/nature10523 Spatio-temporal transcriptome of the human brain Hyo Jung Kang1*, Yuka Imamura Kawasawa1*, Feng Cheng1*, Ying Zhu1*, Xuming Xu1*, Mingfeng Li1*, Andre´ M. M. Sousa1,2, Mihovil Pletikos1,3, Kyle A. Meyer1, Goran Sedmak1,3, Tobias Guennel4, Yurae Shin1, Matthew B. Johnson1,Zˇeljka Krsnik1, Simone Mayer1,5, Sofia Fertuzinhos1, Sheila Umlauf6, Steven N. Lisgo7, Alexander Vortmeyer8, Daniel R. Weinberger9, Shrikant Mane6, Thomas M. Hyde9,10, Anita Huttner8, Mark Reimers4, Joel E. Kleinman9 & Nenad Sˇestan1 Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.
    [Show full text]
  • Effects of Rapamycin on Social Interaction Deficits and Gene
    Kotajima-Murakami et al. Molecular Brain (2019) 12:3 https://doi.org/10.1186/s13041-018-0423-2 RESEARCH Open Access Effects of rapamycin on social interaction deficits and gene expression in mice exposed to valproic acid in utero Hiroko Kotajima-Murakami1,2, Toshiyuki Kobayashi3, Hirofumi Kashii1,4, Atsushi Sato1,5, Yoko Hagino1, Miho Tanaka1,6, Yasumasa Nishito7, Yukio Takamatsu7, Shigeo Uchino1,2 and Kazutaka Ikeda1* Abstract The mammalian target of rapamycin (mTOR) signaling pathway plays a crucial role in cell metabolism, growth, and proliferation. The overactivation of mTOR has been implicated in the pathogenesis of syndromic autism spectrum disorder (ASD), such as tuberous sclerosis complex (TSC). Treatment with the mTOR inhibitor rapamycin improved social interaction deficits in mouse models of TSC. Prenatal exposure to valproic acid (VPA) increases the incidence of ASD. Rodent pups that are exposed to VPA in utero have been used as an animal model of ASD. Activation of the mTOR signaling pathway was recently observed in rodents that were exposed to VPA in utero, and rapamycin ameliorated social interaction deficits. The present study investigated the effect of rapamycin on social interaction deficits in both adolescence and adulthood, and gene expressions in mice that were exposed to VPA in utero. We subcutaneously injected 600 mg/kg VPA in pregnant mice on gestational day 12.5 and used the pups as a model of ASD. The pups were intraperitoneally injected with rapamycin or an equal volume of vehicle once daily for 2 consecutive days. The social interaction test was conducted in the offspring after the last rapamycin administration at 5–6 weeks of ages (adolescence) or 10–11 weeks of age (adulthood).
    [Show full text]
  • Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
    Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7
    [Show full text]
  • Cellular and Molecular Signatures in the Disease Tissue of Early
    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
    [Show full text]
  • Supplemental Information
    Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig.
    [Show full text]
  • Characterization of a Monoclonal Antibody Panel Shows That the Myotonic Dystrophy Protein Kinase, DMPK, Is Expressed Almost Exclusively in Muscle and Heart
    © 2000 Oxford University Press Human Molecular Genetics, 2000, Vol. 9, No. 14 2167–2173 Characterization of a monoclonal antibody panel shows that the myotonic dystrophy protein kinase, DMPK, is expressed almost exclusively in muscle and heart L.T. Lam, Y.C.N. Pham, Nguyen thi Man and G.E. Morris+ MRIC Biochemistry Group, PP18, North East Wales Institute, Mold Road, Wrexham LL11 2AW, UK Received 11 May 2000; Accepted 28 June 2000 DDBJ/EMBL/GenBank accession no. AF250871 Myotonic dystrophy (DM) is a multisystemic disorder ness), brain (mental retardation), eyes (cataracts), testis caused by an inherited CTG repeat expansion which (atrophy) and heart (conduction defects) (10). It is important, affects three genes encoding the DM protein kinase therefore, to establish the normal tissue distribution of all three (DMPK), a homeobox protein Six5 and a protein proteins produced from the DM locus. containing WD repeats. Using a panel of 16 mono- The human DMPK cDNA has 15 exons and predicts a ∼ clonal antibodies against several different DMPK protein of 70 kDa, although there may be variation in the size epitopes we detected DMPK, as a single protein of and sequence of the first exon (1–3,11,12). DMPK is a serine/ threonine protein kinase in which the catalytic domain ∼80 kDa, only in skeletal muscle, cardiac muscle and, (∼43 kDa) is followed by a coiled-coil domain (∼12 kDa) and to a lesser extent, smooth muscle. Many earlier a hydrophobic C-terminal domain. Alternative splicing has reports of DMPK with different sizes and tissue distri- been observed in a short VSGGG sequence between domains butions appear to be due to antibody cross-reactions and in the C-terminal domain (13).
    [Show full text]
  • Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.
    [Show full text]
  • Table S2.Up Or Down Regulated Genes in Tcof1 Knockdown Neuroblastoma N1E-115 Cells Involved in Differentbiological Process Anal
    Table S2.Up or down regulated genes in Tcof1 knockdown neuroblastoma N1E-115 cells involved in differentbiological process analysed by DAVID database Pop Pop Fold Term PValue Genes Bonferroni Benjamini FDR Hits Total Enrichment GO:0044257~cellular protein catabolic 2.77E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 537 13588 1.944851 8.64E-07 8.64E-07 5.02E-07 process ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, USP17L5, FBXO11, RAD23B, NEDD8, UBE2V2, RFFL, CDC GO:0051603~proteolysis involved in 4.52E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 534 13588 1.93519 1.41E-06 7.04E-07 8.18E-07 cellular protein catabolic process ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, USP17L5, FBXO11, RAD23B, NEDD8, UBE2V2, RFFL, CDC GO:0044265~cellular macromolecule 6.09E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 609 13588 1.859332 1.90E-06 6.32E-07 1.10E-06 catabolic process ISG15, RBM8A, ATG7, LOC100046898, PSENEN, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, XRN2, USP17L5, FBXO11, RAD23B, UBE2V2, NED GO:0030163~protein catabolic process 1.81E-09 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 556 13588 1.87839 5.64E-06 1.41E-06 3.27E-06 ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4,
    [Show full text]