This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G

Total Page:16

File Type:pdf, Size:1020Kb

This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. The effects of mutations in the eEF1A2 gene in mouse gene expression profiles and identification of potential markers for motor neuron degeneration. Vesa Qarkaxhija MSc Genetics and Molecular Medicine. University of Edinburgh 2017 Declaration page I declare that this thesis is my own work unless stated otherwise and any inclusions of contributions or publications from others have been clearly acknowledged. This thesis has not been submitted for any other degree or professional qualification. Vesa Qarkaxhija 1 Acknowledgments I would like to thank Cathy Abbott, whose guidance and support has made this year both incredibly enlightening and enjoyable. I have learnt an inexpressible amount from her and could not have asked for a more wonderful supervisor. I would also like to thank the Abbott lab group, who have also taught me so much, in the lab and out, whom I had many a titillating conversations and debates with that made my time here most entertaining. My family and friends both old and new who had enriched this experience and accompanied me on many Scottish adventures during the course of my masters. 2 Contents: Declaration page ......................................................................................................................... 1 Acknowledgments ....................................................................................................................... 2 Abstract: ........................................................................................................................................ 6 Lay Summary: .............................................................................................................................. 7 Chapter 1: Introduction ............................................................................................................. 10 1.1 Translation and Eukaryotic Elongation factor Alpha (eEF1A). ........................... 10 1.2 eEF1A isoforms. ........................................................................................................ 11 1.3 Translation factors and EEF1A in disease. ................................................................ 19 1.4 eEF1A isoforms in Neurological disease. ................................................................... 20 1.5 Motor neuron disease and eEF1A2. ............................................................................ 23 1.6 Mouse Models: ................................................................................................................ 25 1.7 Biomarkers for Motor neuron degeneration. ............................................................... 27 1.8 Project Aims: ................................................................................................................... 29 Chapter 2: Materials and methods. ........................................................................................ 30 2.1: Clustal Alignments and Phylogenetic trees. .............................................................. 30 2.2: Genotyping. .................................................................................................................... 30 2.2.1: Genomic DNA extraction. ..................................................................................... 30 2.2.2: Nested PCR. ........................................................................................................... 30 2.2.3: TOPO Cloning. ....................................................................................................... 31 2.3: Protein expression. ........................................................................................................ 31 2.3.1: Sample preparation................................................................................................ 31 2.3.2: Protein separation. ................................................................................................. 31 2.3.3: Protein transfer, immunoblotting and quantification. ........................................ 31 2.4: Immunohistochemistry. ................................................................................................. 32 2.4.1: Sample preparation................................................................................................ 32 2.4.1: Section staining. ..................................................................................................... 32 2.5: Proteomics. ..................................................................................................................... 33 2.5.1: Sample extraction and preparation. .................................................................... 33 2.5.2: Mass spectrometry. ................................................................................................ 33 3 2.5.3: Statistical tests. ....................................................................................................... 34 2.6: Microarray. ...................................................................................................................... 34 2.6.1: Sample extraction and preparation. .................................................................... 34 2.6.2: Statistical tests. ....................................................................................................... 34 2.7: Gene Ontology analysis. .............................................................................................. 34 Chapter 3: Characterisation of the D252H mutation in eEF1A2 in mice. ......................... 35 3.1 Development of mouse lines. ....................................................................................... 35 3.2 Genotyping. ..................................................................................................................... 36 3.3 Protein Expression. ........................................................................................................ 40 3.4 Discussion. ...................................................................................................................... 44 Chapter 4: Analysis of eEF1A2 null and Wildtype spinal cord quantitative proteome and neuronal damage. ..................................................................................................................... 47 4.1 Biological specimens. .................................................................................................... 47 4.2 Establishing the extent of neurodegeneration in the new homozygous mice. ...... 48 4.3 Mass Spectrometry. ....................................................................................................... 52 4.3.1 Quantitative Label-free Mass spectrometry. ........................................................... 52 4.3.2 Peptide identification. .............................................................................................. 53 4.3.3 Determination of protein abundances. ................................................................. 54 4.4 Data Analysis. ................................................................................................................. 56 4.4.1 Expression profiles: ................................................................................................. 58 4.5 Gene Ontology analysis: ............................................................................................... 64 4.6 Discussion........................................................................................................................ 69 Chapter 5: RNA expression profiles of wasted mice and wild type mice. ........................ 75 5.1 Biological specimens. .................................................................................................... 76 5.2 Expression profiles. ........................................................................................................ 77 5.3 Gene Ontology analysis. ............................................................................................... 81 5.4 Discussion........................................................................................................................ 86 Chapter 6: Proteins and genes in the context of MND. ....................................................... 88 6.1 Fibroblast growth factor 12 (Fgf12).............................................................................. 88 6.1 Cytoplasmic dynein 1 intermediate chain 2 (Dync1i2). ............................................. 89 6.2 DnaJ homolog subfamily C member 5 (Dnajc5). ....................................................... 92 6.3 Kinesin heavy chain isoform 5C (Kif5C). .................................................................... 95 4 6.4 Discussion. .....................................................................................................................
Recommended publications
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Downloaded and Searched Against the Dbest Database to Identify Ests
    BMC Genomics BioMed Central Research article Open Access A transcription map of the 6p22.3 reading disability locus identifying candidate genes Eric R Londin1, Haiying Meng2 and Jeffrey R Gruen*2 Address: 1Graduate Program in Genetics, State University of New York at Stony Brook, NY, USA and 2Yale Child Health Research Center, Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA Email: Eric R Londin - [email protected]; Haiying Meng - [email protected]; Jeffrey R Gruen* - [email protected] * Corresponding author Published: 30 June 2003 Received: 22 April 2003 Accepted: 30 June 2003 BMC Genomics 2003, 4:25 This article is available from: http://www.biomedcentral.com/1471-2164/4/25 © 2003 Londin et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. reading disabilitydyslexia6p22.3In silicoESTs Abstract Background: Reading disability (RD) is a common syndrome with a large genetic component. Chromosome 6 has been identified in several linkage studies as playing a significant role. A more recent study identified a peak of transmission disequilibrium to marker JA04 (G72384) on chromosome 6p22.3, suggesting that a gene is located near this marker. Results: In silico cloning was used to identify possible candidate genes located near the JA04 marker. The 2 million base pairs of sequence surrounding JA04 was downloaded and searched against the dbEST database to identify ESTs. In total, 623 ESTs from 80 different tissues were identified and assembled into 153 putative coding regions from 19 genes and 2 pseudogenes encoded near JA04.
    [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]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • Mitoxplorer, a Visual Data Mining Platform To
    mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, Cecilia Garcia-Perez, José Villaveces, Salma Gamal, Giovanni Cardone, Fabiana Perocchi, et al. To cite this version: Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, et al.. mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dy- namics and mutations. Nucleic Acids Research, Oxford University Press, 2020, 10.1093/nar/gkz1128. hal-02394433 HAL Id: hal-02394433 https://hal-amu.archives-ouvertes.fr/hal-02394433 Submitted on 4 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Nucleic Acids Research, 2019 1 doi: 10.1093/nar/gkz1128 Downloaded from https://academic.oup.com/nar/advance-article-abstract/doi/10.1093/nar/gkz1128/5651332 by Bibliothèque de l'université la Méditerranée user on 04 December 2019 mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim1,†, Prasanna Koti1,†, Adrien Bonnard2, Fabio Marchiano3, Milena Durrbaum¨ 1, Cecilia Garcia-Perez4, Jose Villaveces1, Salma Gamal1, Giovanni Cardone1, Fabiana Perocchi4, Zuzana Storchova1,5 and Bianca H.
    [Show full text]
  • Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
    Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing
    [Show full text]
  • Mass Spectrometry Discovery-Based Proteomics to Examine Anti-Aging Effects of the Nutraceutical NT-020 in Rat Serum" (2020)
    University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School March 2020 Mass Spectrometry Discovery-Based Proteomics to Examine Anti- Aging Effects of the Nutraceutical NT-020 in Rat Serum Samantha M. Portis University of South Florida Follow this and additional works at: https://scholarcommons.usf.edu/etd Part of the Bioinformatics Commons, and the Neurosciences Commons Scholar Commons Citation Portis, Samantha M., "Mass Spectrometry Discovery-Based Proteomics to Examine Anti-Aging Effects of the Nutraceutical NT-020 in Rat Serum" (2020). Graduate Theses and Dissertations. https://scholarcommons.usf.edu/etd/8279 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Mass Spectrometry Discovery-Based Proteomics to Examine Anti-Aging Effects of the Nutraceutical NT-020 in Rat Serum by Samantha M. Portis A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Medical Sciences with a concentration in Neuroscience Department of Molecular Pharmacology and Physiology College of Medicine University of South Florida Major Professor: Paul R. Sanberg, Ph.D., D.Sc Co-Major Professor: Paula C. Bickford, Ph.D. Kevin Nash, Ph.D. Dominic D’Agostino, Ph.D. Brent Small, Ph.D. Date of Approval: March 28, 2020 Keywords: aging, proteome, serum, inflammation, bioinformatics Copyright © 2020, Samantha M. Portis Dedication I dedicate this work to my family, Alan, Candace, Carolyn, and Jimmy, my brother, Patrick, and my partner, Will.
    [Show full text]
  • Amy Elizabeth Defnet Contact Information: [email protected] Degree and Date to Be Conferred: Ph.D
    Targeting the Activator Protein-1 Complex to Inhibit Airway Smooth Muscle Cell Hyperproliferation in Asthma Item Type dissertation Authors Defnet, Amy Elizabeth Publication Date 2021 Abstract Hyperproliferation of airway smooth muscle (ASM) cells leads to increased ASM mass causing airway obstruction in inflammatory diseases such as asthma. Currently, there are no effective therapies to modulate ASM cell proliferation that contributes to ... Keywords Activator Protein-1; airway smooth muscle; retinoic acid; Airway Remodeling; Asthma; Protein Kinases; Transcription Factor AP-1; Tretinoin Download date 29/09/2021 14:19:54 Link to Item http://hdl.handle.net/10713/15769 Amy Elizabeth Defnet Contact Information: [email protected] Degree and Date to be Conferred: Ph.D. Pharmaceutical Sciences, May 2021 PROFESSIONAL OBJECTIVE My career objective is to work in academic institution where I can develop myself as an educator and researcher. My research employs a cross-disciplinary training regimen, including frequent opportunities for scientific/public speaking and inter-departmental engagement. In preparation for future teaching responsibilities, I have cultivated core pedagogical techniques through the JHU-UMB Collaborative Teaching Fellowship and Quality Matters Online Teaching Program. Additionally, participation in several societies and volunteer groups have helped cultivate my leadership and communication skills. EDUCATION University of Maryland, Baltimore 2016-present • Ph.D. Pharmaceutical Sciences, anticipated completion 2021 Fairleigh Dickinson University, Florham 2012-2016 • B.S. Biological Sciences with a Minor in Chemistry, 2016 RESEARCH Graduate Research Dr. Paul Shapiro and Dr. Maureen Kane, University of Maryland, Baltimore Fall 2016- Present This study hopes to overcome therapeutic limitations in asthma treatment that lead to bronchoconstriction and airway remodeling through evaluation of a novel function- selective ERK1/2 inhibitor and a RAR agonist.
    [Show full text]
  • Split-Turboid Enables Contact-Dependent Proximity Labeling in Cells
    Split-TurboID enables contact-dependent proximity labeling in cells Kelvin F. Choa, Tess C. Branonb,c,d,e, Sanjana Rajeevb, Tanya Svinkinaf, Namrata D. Udeshif, Themis Thoudamg, Chulhwan Kwakh,i, Hyun-Woo Rheeh,j, In-Kyu Leeg,k,l, Steven A. Carrf, and Alice Y. Tingb,c,d,m,1 aCancer Biology Program, Stanford University, Stanford, CA 94305; bDepartment of Genetics, Stanford University, Stanford, CA 94305; cDepartment of Biology, Stanford University, Stanford, CA 94305; dDepartment of Chemistry, Stanford University, Stanford, CA 94305; eDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139; fBroad Institute of MIT and Harvard, Cambridge, MA 02142; gResearch Institute of Aging and Metabolism, Kyungpook National University, 37224 Daegu, South Korea; hDepartment of Chemistry, Seoul National University, 08826 Seoul, South Korea; iDepartment of Chemistry, Ulsan National Institute of Science and Technology, 44919 Ulsan, South Korea; jSchool of Biological Sciences, Seoul National University, 08826 Seoul, South Korea; kDepartment of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, 41944 Daegu, South Korea; lLeading-edge Research Center for Drug Discovery and Development for Diabetes and Metabolic Disease, Kyungpook National University, 41944 Daegu, South Korea; and mChan Zuckerberg Biohub, San Francisco, CA 94158 Edited by Tony Hunter, The Salk Institute for Biological Studies, La Jolla, CA, and approved April 7, 2020 (received for review November 7, 2019) Proximity labeling catalyzed by promiscuous enzymes, such as Split forms of APEX (18) and BioID (19–21) have previously TurboID, have enabled the proteomic analysis of subcellular regions been reported. However, split-APEX (developed by us) has not difficult or impossible to access by conventional fractionation-based ap- been used for proteomics, and the requirement for exogenous proaches.
    [Show full text]
  • SNV Discovery and Functional Candidate Gene Identification For
    RESEARCH ARTICLE SNV discovery and functional candidate gene identification for milk composition based on whole genome resequencing of Holstein bulls with extremely high and low breeding values Shan Lin1, Hongyan Zhang1, Yali Hou2, Lin Liu3, Wenhui Li1, Jianping Jiang1, Bo Han1, 1 1 Shengli Zhang , Dongxiao SunID * a1111111111 1 Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal a1111111111 Breeding, China Agricultural University, Beijing, China, 2 Laboratory of Disease Genomics and Individualized a1111111111 Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China, 3 Beijing Dairy Cattle a1111111111 Center, Beijing, China a1111111111 * [email protected] Abstract OPEN ACCESS Citation: Lin S, Zhang H, Hou Y, Liu L, Li W, Jiang We have sequenced the whole genomes of eight proven Holstein bulls from the four half-sib J, et al. (2019) SNV discovery and functional or full-sib families with extremely high and low estimated breeding values (EBV) for milk pro- candidate gene identification for milk composition tein percentage (PP) and fat percentage (FP) using Illumina re-sequencing technology. based on whole genome resequencing of Holstein Consequently, 2.3 billion raw reads were obtained with an average effective depth of 8.1 . bulls with extremely high and low breeding values. × PLoS ONE 14(8): e0220629. https://doi.org/ After single nucleotide variant (SNV) calling, total 10,961,243 SNVs were identified, and 10.1371/journal.pone.0220629 57,451 of them showed opposite fixed sites between the bulls with high and low EBVs within Editor: Sujan Mamidi, HudsonAlpha Institute for each family (called as common differential SNVs).
    [Show full text]
  • Frequent Loss-Of-Heterozygosity in CRISPR-Cas9–Edited Early Human Embryos
    Frequent loss-of-heterozygosity in CRISPR-Cas9–edited COLLOQUIUM PAPER early human embryos Gregorio Alanis-Lobatoa, Jasmin Zohrenb, Afshan McCarthya, Norah M. E. Fogartya,c, Nada Kubikovad,e, Emily Hardmana, Maria Grecof, Dagan Wellsd,g, James M. A. Turnerb, and Kathy K. Niakana,h,1 aHuman Embryo and Stem Cell Laboratory, The Francis Crick Institute, NW1 1AT London, United Kingdom; bSex Chromosome Biology Laboratory, The Francis Crick Institute, NW1 1AT London, United Kingdom; cCentre for Stem Cells and Regenerative Medicine, Guy’s Campus, King’s College London, SE1 9RT London, United Kingdom; dNuffield Department of Women’s and Reproductive Health, John Radcliffe Hospital, University of Oxford, OX3 9DU Oxford, United Kingdom; eJesus College, University of Oxford, OX1 3DW Oxford, United Kingdom; fAncient Genomics Laboratory, The Francis Crick Institute, NW1 1AT London, United Kingdom; gJuno Genetics, OX4 4GE Oxford, United Kingdom; and hThe Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3EG Cambridge, United Kingdom Edited by Barbara J. Meyer, University of California, Berkeley, CA, and approved October 31, 2020 (received for review June 5, 2020) CRISPR-Cas9 genome editing is a promising technique for clinical homozygous WT embryos in both cases was not associated with applications, such as the correction of disease-associated alleles in use of the provided repair template for gene correction. Instead, somatic cells. The use of this approach has also been discussed in the authors suggest that in edited embryos the WT maternal the context of heritable editing of the human germ line. However, allele served as a template for the high-fidelity homology di- studies assessing gene correction in early human embryos report rected repair (HDR) pathway to repair the double-strand lesion low efficiency of mutation repair, high rates of mosaicism, and the caused by the Cas9 protein in the paternal allele (8).
    [Show full text]
  • Identification of Core Genes Involved in the Progression of Cervical
    International Journal of Molecular Sciences Article Identification of Core Genes Involved in the Progression of Cervical Cancer Using an Integrative mRNA Analysis Marina Dudea-Simon 1, Dan Mihu 1, Alexandru Irimie 2,3, Roxana Cojocneanu 4, Schuyler S. Korban 5, Radu Oprean 6, Cornelia Braicu 4,* and Ioana Berindan-Neagoe 4,7 1 2nd Obstetrics and Gynecology Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; [email protected] (M.D.-S.); [email protected] (D.M.) 2 Department of Surgery, “Prof. Dr. Ion Chiricuta” Oncology Institute, 400015 Cluj-Napoca, Romania; [email protected] 3 Department of Surgical Oncology and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania 4 Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 23 Marinescu Street, 400337 Cluj-Napoca, Romania; [email protected] (R.C.); [email protected] (I.B.-N.) 5 Department of Natural and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; [email protected] 6 Analytical Chemistry Department, Iuliu Hatieganu University of Medicine and Pharmacy, 4, Louis Pasteur Street, 400349 Cluj-Napoca, Romania; [email protected] 7 Department of Functional Genomics and Experimental Pathology, “Prof. Dr. Ion Chiricu¸tă” Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania * Correspondence: [email protected] Received: 5 September 2020; Accepted: 1 October 2020; Published: 3 October 2020 Abstract: In spite of being a preventable disease, cervical cancer (CC) remains at high incidence, and it has a significant mortality rate. Although hijacking of the host cellular pathway is fundamental for developing a better understanding of the human papillomavirus (HPV) pathogenesis, a major obstacle is identifying the central molecular targets involved in HPV-driven CC.
    [Show full text]