DOCSLIB.ORG

Gene Expression Network Analysis Provides Potential Targets Against SARS-Cov-2

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Gene Expression Network Analysis Provides Potential Targets Against SARS-Cov-2 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.182634; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Gene Expression Network Analysis Provides Potential Targets Against SARS-CoV-2 2 Ana I. Hernández Cordero1*, Xuan Li1, Chen Xi Yang1, Stephen Milne1,2,3, Yohan Bossé4, Philippe 3 Joubert4, Wim Timens5, Maarten van den Berge6, David Nickle7, Ke Hao8, Don D. Sin1,2 4 5 1 Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada 6 2 Division of Respiratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, 7 BC, Canada 8 3 Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia 9 4 Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec City, 10 QC, Canada 11 5 University of Groningen, University Medical Centre Groningen, Department of Pathology and 12 Medical Biology, Groningen, The Netherlands 13 6 University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, 14 Groningen, The Netherlands 15 7 Merck Research Laboratories, Genetics and Pharmacogenomics, Boston, Massachusetts, United 16 States of America 17 8 Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic 18 Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America 19 20 * Corresponding author 21 E-mail: [email protected] 22 23 24 25 26 27 28 29 30 31 32 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.182634; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 33 ABSTRACT 34 BACKGROUND: Cell entry of SARS-CoV-2, the novel coronavirus causing COVID-19, is facilitated 35 by host cell angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 36 (TMPRSS2). We aimed to identify and characterize genes that are co-expressed with ACE2 and 37 TMPRSS2, and to further explore their biological functions and potential as druggable targets. 38 METHODS: Using the gene expression profiles of 1,038 lung tissue samples, we performed a 39 weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. We 40 explored the biology of co-expressed genes using bioinformatics databases, and identified known 41 drug-gene interactions. RESULTS: ACE2 was in a module of 681 co-expressed genes; 12 genes with 42 moderate-high correlation with ACE2 (r>0.3, FDR<0.05) had known interactions with existing drug 43 compounds. TMPRSS2 was in a module of 1,086 co-expressed genes; 15 of these genes were enriched 44 in the gene ontology biologic process ‘Entry into host cell’, and 53 TMPRSS2-correlated genes had 45 known interactions with drug compounds. CONCLUSION: Dozens of genes are co-expressed with 46 ACE2 and TMPRSS2, many of which have plausible links to COVID-19 pathophysiology. Many of 47 the co-expressed genes are potentially targetable with existing drugs, which may help to fast-track the 48 development of COVID-19 therapeutics. 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.182634; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 67 INTRODUCTION 68 69 Since the outbreak of severe acute respiratory syndrome coronavirus (SARS coronavirus) in 70 2002 and 2003, coronaviruses have been considered to be highly pathogenic for humans 1–3. A new 71 coronavirus (SARS-CoV-2) that spreads through respiratory droplets emerged in December of 2019 72 in Wuhan, Hubei province, China 4. Its rapid spread across China and the rest of the world ultimately 73 resulted in the global COVID-19 pandemic, which was officially declared in March 2020 by the World 74 Health Organization (WHO). 75 76 It has since been shown that SARS-CoV-2 shares a common host cell entry mechanism with 77 the 2002-2003 SARS coronavirus 5,6. The viral genome encodes for multiple viral components, 78 including the spike protein (S), which facilitates viral entry into the host cell 7,8. The S protein 79 associates with the angiotensin-converting enzyme 2 (ACE2) to mediate infection of the target cells9. 80 ACE2 is a type 1 transmembrane metallocarboxypeptidase that is an important negative regulator of 81 the renin–angiotensin system (RAS). Once SARS-CoV-2 gains entry into epithelial cells, surface 82 expression of ACE2 is rapidly downregulated in the infected cell 10, which leads to an imbalance in 83 angiotensin II-mediated signalling and predisposes the host to acute lung injury. SARS-CoV-2 also 84 employs transmembrane serine protease 2 (TMPRSS2) to proteolytically activate the S protein, which 85 is essential for viral entry into target cells 11. 86 87 In view of the rapid spread and the mortality of COVID-19 worldwide, there is an urgent need 88 to find effective treatments against this infection, especially for severe cases. However, the 89 development of a vaccine and novel treatments may take months to years, requiring billions of dollars 90 in investment and with no certainty of their ultimate success. Bioinformatic approaches, however, can 91 rapidly identify relevant gene-drug interactions that may contribute to the understanding of the 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.182634; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 92 mechanisms of viral infection and reduce the time to finding potential drug targets and existing drugs 93 that could be repurposed for this indication. Here, we performed a gene expression network analysis 94 on data generated in the Lung eQTL Consortium Cohort to investigate the mechanisms of ACE2 and 95 TMPRSS2 expression in lung tissue. We identified potential targets to be explored as possible 96 treatments for COVID-19. We hypothesized that the mechanisms associated with ACE2 and TMPRSS2 97 likely encompass protein coding genes involved in the pathogenesis of COVID-19. 98 99 RESULTS 100 The Lung eQTL Consortium cohort used in this gene network analysis is described in Table 101 1. Supplementary Fig. S1 shows the expression levels of ACE2 and TMPRSS2 in the three centres 102 that are part of the Lung eQTL Consortium (see methods); ACE2 had low to moderate expression 103 levels in lung tissue; whereas TMPRSS2 was highly expressed. Based on the study cohort lung 104 expression profile, we determined that ACE2 and TMPRSS2 were contained in distinct modules. The 105 module containing ACE2 (ACE2 module) included 681 unique genes, while the modules containing 106 TMPRSS2 (TMPRSS2 module) encompassed 1,086 unique genes (Supplementary Tables S1 and S2). 107 Only 41 genes were found in both modules. The hub gene for the ACE2 module was TMEM33, and 108 hub gene for the TMPRSS2 module was PDZD2 (see methods for the definition of ‘hub gene’). Fig. 1 109 shows the top 50 genes with the highest connectivity to ACE2 and TMPRSS2 within their respective 110 modules, based on the weighted gene correlation network analysis (WGCNA) analysis. 111 112 Table 1. Study cohort demographics Lung eQTL Consortium Cohort n 1,038 Age, years† 61 (52-69) Females, n (%) 472 (45.47) BMI, kg/m2† 24.60 (21.80-27.98) COPD‡, n (%) 426 (41.04) Asthma, n (%) 37 (3.56) 4 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.182634; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Cardiac disease, n (%) 192 (18.50) Hypertension, n (%) 142 (13.68) Diabetes, n (%) 81 (7.80) Never smokers, n (%) 162 (15.61) Former smokers, n (%) 631 (60.79) Current smokers, n (%) 245 (23.60) 113 †Median (interquartile range). ‡ chronic obstructive pulmonary disease 114 115 116 Figure 1. ACE2 and TMPRSS2 expression modules. The center of each graph represents ACE2 (A) 117 or TMPRSS2 (B), the circles at the edges represent the top 50 genes with the highest connectivity to 118 ACE2 or TMPRSS2 based on the WGCNA analysis. The circle size represents the size of each gene 119 node in their respective modules. The arm thickness represents the relative strength of the connection 120 to the ACE2 or TMPRSS2 expression. 121 122 ACE2 module 123 124 The median module membership (MM) (see methods) across the genes in the ACE2 module 125 was 0.40, and the minimum and maximum values were 0.002 and 0.79, respectively. The MM for 126 ACE2 was 0.25. We utilized genes in the ACE2 module to execute a pathway enrichment analysis, 127 which showed significant enrichment of four Kyoto Encyclopedia of Genes and Genomes (KEGG) 128 pathways (Lysosome, Metabolic pathways, N-Glycan biosynthesis and Endocytosis) (Supplementary 5 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.182634; this version posted July 6, 2020.
Load more

Recommended publications
  • Mouse Pign Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Pign Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Pign conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Pign gene (NCBI Reference Sequence: NM_013784 ; Ensembl: ENSMUSG00000056536 ) is located on Mouse chromosome 1. 31 exons are identified, with the ATG start codon in exon 4 and the TGA stop codon in exon 31 (Transcript: ENSMUST00000186485). Exon 8 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Pign gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-213N12 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for an ENU-induced allele exhibit abnormal gastrulation, forebrain hypoplasia, coloboma, and microphthalmia. Exon 8 starts from about 19.69% of the coding region. The knockout of Exon 8 will result in frameshift of the gene. The size of intron 7 for 5'-loxP site insertion: 3696 bp, and the size of intron 8 for 3'-loxP site insertion: 1043 bp. The size of effective cKO region: ~625 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 8 9 31 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Pign Homology arm cKO region loxP site Page 2 of 8 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.
    [Show full text]
  • Congenital Disorders of Glycosylation from a Neurological Perspective
    brain sciences Review Congenital Disorders of Glycosylation from a Neurological Perspective Justyna Paprocka 1,* , Aleksandra Jezela-Stanek 2 , Anna Tylki-Szyma´nska 3 and Stephanie Grunewald 4 1 Department of Pediatric Neurology, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland 2 Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [email protected] 3 Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, W 04-730 Warsaw, Poland; [email protected] 4 NIHR Biomedical Research Center (BRC), Metabolic Unit, Great Ormond Street Hospital and Institute of Child Health, University College London, London SE1 9RT, UK; [email protected] * Correspondence: [email protected]; Tel.: +48-606-415-888 Abstract: Most plasma proteins, cell membrane proteins and other proteins are glycoproteins with sugar chains attached to the polypeptide-glycans. Glycosylation is the main element of the post- translational transformation of most human proteins. Since glycosylation processes are necessary for many different biological processes, patients present a diverse spectrum of phenotypes and severity of symptoms. The most frequently observed neurological symptoms in congenital disorders of glycosylation (CDG) are: epilepsy, intellectual disability, myopathies, neuropathies and stroke-like episodes. Epilepsy is seen in many CDG subtypes and particularly present in the case of mutations
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [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]
  • A Genome-Wide Association Study of Calf Birth Weight in Holstein Cattle Using Single Nucleotide Polymorphisms and Phenotypes Predicted from Auxiliary Traits
    J. Dairy Sci. 97 :3156–3172 http://dx.doi.org/ 10.3168/jds.2013-7409 © American Dairy Science Association®, 2014 . A genome-wide association study of calf birth weight in Holstein cattle using single nucleotide polymorphisms and phenotypes predicted from auxiliary traits J. B. Cole ,*1 B. Waurich ,† M. Wensch-Dorendorf ,† D. M. Bickhart ,* and H. H. Swalve † * Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350 † Institute of Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Theodor-Lieser-Str. 11, D-06120 Halle / Saale, Germany ABSTRACT derived from one population are useful for identifying genes and gene networks associated with phenotypes Previous research has found that a quantitative trait that are not directly measured in a second population. locus exists affecting calving and conformation traits This approach will identify only genes associated with on Bos taurus autosome 18 that may be related to the traits used to construct the birth weight predictor, increased calf birth weights, which are not routinely and not loci that affect only birth weight. recorded in the United States. Birth weight data from Key words: birth weight , quantitative trait loci , se- large, intensively managed dairies in eastern Germany lection index , single nucleotide polymorphism with management systems similar to those commonly found in the United States were used to develop a selec- INTRODUCTION tion index predictor for predicted transmitting ability (PTA) of birth weight. The predictor included body Many studies have reported on QTL affecting calving depth, rump width, sire calving ease, sire gestation traits in several populations of Holstein cattle (Kühn et al., 2003; Schnabel et al., 2005; Holmberg and Ander- length, sire stillbirth, stature, and strength.
    [Show full text]
  • A Novel Resveratrol Analog: Its Cell Cycle Inhibitory, Pro-Apoptotic and Anti-Inflammatory Activities on Human Tumor Cells
    A NOVEL RESVERATROL ANALOG : ITS CELL CYCLE INHIBITORY, PRO-APOPTOTIC AND ANTI-INFLAMMATORY ACTIVITIES ON HUMAN TUMOR CELLS A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Boren Lin May 2006 Dissertation written by Boren Lin B.S., Tunghai University, 1996 M.S., Kent State University, 2003 Ph. D., Kent State University, 2006 Approved by Dr. Chun-che Tsai , Chair, Doctoral Dissertation Committee Dr. Bryan R. G. Williams , Co-chair, Doctoral Dissertation Committee Dr. Johnnie W. Baker , Members, Doctoral Dissertation Committee Dr. James L. Blank , Dr. Bansidhar Datta , Dr. Gail C. Fraizer , Accepted by Dr. Robert V. Dorman , Director, School of Biomedical Sciences Dr. John R. Stalvey , Dean, College of Arts and Sciences ii TABLE OF CONTENTS LIST OF FIGURES……………………………………………………………….………v LIST OF TABLES……………………………………………………………………….vii ACKNOWLEDGEMENTS….………………………………………………………….viii I INTRODUCTION….………………………………………………….1 Background and Significance……………………………………………………..1 Specific Aims………………………………………………………………………12 II MATERIALS AND METHODS.…………………………………………….16 Cell Culture and Compounds…….……………….…………………………….….16 MTT Cell Viability Assay………………………………………………………….16 Trypan Blue Exclusive Assay……………………………………………………...18 Flow Cytometry for Cell Cycle Analysis……………..……………....……………19 DNA Fragmentation Assay……………………………………………...…………23 Caspase-3 Activity Assay………………………………...……….….…….………24 Annexin V-FITC Staining Assay…………………………………..…...….………28 NF-kappa B p65 Activity Assay……………………………………..………….…29
    [Show full text]
  • High Mutation Frequency of the PIGA Gene in T Cells Results In
    High Mutation Frequency of the PIGA Gene in T Cells Results in Reconstitution of GPI A nchor−/CD52− T Cells That Can Give Early Immune Protection after This information is current as Alemtuzumab-Based T Cell−Depleted of October 1, 2021. Allogeneic Stem Cell Transplantation Floris C. Loeff, J. H. Frederik Falkenburg, Lois Hageman, Wesley Huisman, Sabrina A. J. Veld, H. M. Esther van Egmond, Marian van de Meent, Peter A. von dem Borne, Hendrik Veelken, Constantijn J. M. Halkes and Inge Jedema Downloaded from J Immunol published online 2 February 2018 http://www.jimmunol.org/content/early/2018/02/02/jimmun ol.1701018 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2018/02/02/jimmunol.170101 Material 8.DCSupplemental Why The JI? Submit online. by guest on October 1, 2021 • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2018 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published February 2, 2018, doi:10.4049/jimmunol.1701018 The Journal of Immunology High Mutation Frequency of the PIGA Gene in T Cells Results in Reconstitution of GPI Anchor2/CD522 T Cells That Can Give Early Immune Protection after Alemtuzumab- Based T Cell–Depleted Allogeneic Stem Cell Transplantation Floris C.
    [Show full text]
  • Whole Genome Sequencing of Nearly Isogeneic WMI and WLI Inbred Rats Identifies Genes Potentially Involved in Depression
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.04.411769; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Whole genome sequencing of nearly isogeneic WMI and WLI inbred rats identifies genes potentially involved in depression Tristan de Jong, Panjun Kim, Victor Guryev, Megan Mulligan, Robert W Williams, Eva E Redei, Hao Chen Abstract Background: The WMI and WLI inbred rat substrains were generated from the stress-prone, and not yet fully inbred, Wistar Kyoto (WKY) strain using bi-directional selection for immobility in the forced swim test followed by over 38 generations of inbreeding. Despite the low level of genetic diversity among WKY progenitors, the WMI substrain is more vulnerable to stress relative to its WLI control substrain. Here we quantify numbers and classes of sequence variants distinguishing these substrains and test the hypothesis that they are nearly isogenic. Results: The WLI and WMI genomic DNA were sequenced using Illumina xTen, IonTorrent and 10X Chromium technologies to obtain a combined coverage of over 100X. We identified 4,296 high quality homozygous SNPs and indels that differ between the WMI and WLI substrains. Gene ontology analysis of these variants showed an enrichment for neurogenesis related pathways. In addition, high impact variations were detected in genes previously implicated in depression (e.g. Gnat2), depression-like behavior (e.g. Prlr, Nlrp1a), other psychiatric disease (e.g.
    [Show full text]
  • Hypotonia and Intellectual Disability Without Dysmorphic Features in A
    Thiffault et al. BMC Medical Genetics (2017) 18:124 DOI 10.1186/s12881-017-0481-9 CASE REPORT Open Access Hypotonia and intellectual disability without dysmorphic features in a patient with PIGN-related disease Isabelle Thiffault1,2,3* , Britton Zuccarelli4, Holly Welsh4, Xuan Yuan5, Emily Farrow1, Lee Zellmer1, Neil Miller1, Sarah Soden1,3,4, Ahmed Abdelmoity4, Robert A. Brodsky5 and Carol Saunders1,2,3 Abstract Background: Defects in the human glycosylphosphatidylinositol anchor biosynthetic pathway are associated with inherited glycosylphosphatidylinositol (GPI)-deficiencies characterized by a broad range of clinical phenotypes including multiple congenital anomalies, dysmorphic faces, developmental delay, hypotonia, and epilepsy. Biallelic variants in PIGN, encoding phosphatidylinositol-glycan biosynthesis class N have been recently associated with multiple congenital anomalies hypotonia seizure syndrome. Case presentation: Our patient is a 2 year old male with hypotonia, global developmental delay, and focal epilepsy. Trio whole-exome sequencing revealed heterozygous variants in PIGN, c.181G > T (p.Glu61*) and c.284G > A (p.Arg95Gln). Analysis of FLAER and anti-CD59 by flow-cytometry demonstrated a shift in this patient’s granulocytes, confirming a glycosylphosphatidylinositol-biosynthesis defect, consistent with PIGN-related disease. Conclusions: To date, a total of 18 patients have been reported, all but 2 of whom have congenital anomalies and/or obvious dysmorphic features. Our patient has no significant dysmorphic features or multiple congenital anomalies, which is consistent with recent reports linking non-truncating variants with a milder phenotype, highlighting the importance of functional studies in interpreting sequence variants. Keywords: PIGN, Developmental disorders, Intellectual disability, GPI deficiency, Seizures Background GPI proteins with important roles in embryogenesis require Biallelic variants in glycosylphosphatidylinositol (GPI)-an- GPI anchoring for expression on the cell surface [3].
    [Show full text]
  • Genome-Wide Association and Gene Enrichment Analyses of Meat Sensory Traits in a Crossbred Brahman-Angus
    Proceedings of the World Congress on Genetics Applied to Livestock Production, 11. 124 Genome-wide association and gene enrichment analyses of meat tenderness in an Angus-Brahman cattle population J.D. Leal-Gutíerrez1, M.A. Elzo1, D. Johnson1 & R.G. Mateescu1 1 University of Florida, Department of Animal Sciences, 2250 Shealy Dr, 32608 Gainesville, Florida, United States. [email protected] Summary The objective of this study was to identify genomic regions associated with meat tenderness related traits using a whole-genome scan approach followed by a gene enrichment analysis. Warner-Bratzler shear force (WBSF) was measured on 673 steaks, and tenderness and connective tissue were assessed by a sensory panel on 496 steaks. Animals belong to the multibreed Angus-Brahman herd from University of Florida and range from 100% Angus to 100% Brahman. All animals were genotyped with the Bovine GGP F250 array. Gene enrichment was identified in two pathways; the first pathway is involved in negative regulation of transcription from RNA polymerase II, and the second pathway groups several cellular component of the endoplasmic reticulum membrane. Keywords: tenderness, gene enrichment, regulation of transcription, cell growth, cell proliferation Introduction Identification of quantitative trait loci (QTL) for any complex trait, including meat tenderness, is the first most important step in the process of understanding the genetic architecture underlying the phenotype. Given a large enough population and a dense coverage of the genome, a genome-wide association study (GWAS) is usually successful in uncovering major genes and QTLs with large and medium effect on these type of traits. Several GWA studies on Bos indicus (Magalhães et al., 2016; Tizioto et al., 2013) or crossbred beef cattle breeds (Bolormaa et al., 2011b; Hulsman Hanna et al., 2014; Lu et al., 2013) were successful at identifying QTL for meat tenderness; and most of them include the traditional candidate genes µ-calpain and calpastatin.
    [Show full text]
  • Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders
    Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders Andrew H. Chiang Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2021 © 2021 Andrew H. Chiang All Rights Reserved Abstract Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders Andrew H. Chiang Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity. Despite recent progress in ASD genetics, the nature of phenotypic heterogeneity across probands is not well understood. Notably, likely gene- disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. We find that truncating mutations in a gene can result in a range of relatively mild decreases (15-30%) in gene expression due to nonsense-mediated decay (NMD), and show that more severe autism phenotypes are associated with greater decreases in expression. We also find that each gene with recurrent ASD mutations can be described by a parameter, phenotype dosage sensitivity (PDS), which characteriZes the relationship between changes in a gene’s dosage and changes in a given phenotype. Using simple linear models, we show that changes in gene dosage account for a substantial fraction of phenotypic variability in ASD. We further observe that LGD mutations affecting the same exon frequently lead to strikingly similar phenotypes in unrelated ASD probands. These patterns are observed for two independent proband cohorts and multiple important ASD-associated phenotypes. The observed phenotypic similarities are likely mediated by similar changes in gene dosage and similar perturbations to the relative expression of splicing isoforms.
    [Show full text]
  • Demographic History and Genetic Adaptation in the Himalayan
    Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations Elena Arciero,†,1 Thirsa Kraaijenbrink,†,2 Asan,†,3 Marc Haber,1 Massimo Mezzavilla,1,4 Qasim Ayub,1,5,6 Wei Wang,3 Zhaxi Pingcuo,7 Huanming Yang,3,8 Jian Wang,3,8 Mark A. Jobling,9 George van Driem,10 Yali Xue,1 Peter de Knijff,*,2 and Chris Tyler-Smith*,1 1The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom Downloaded from https://academic.oup.com/mbe/article-abstract/35/8/1916/4999976 by Leiden University / LUMC user on 22 July 2019 2Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands 3BGI-Shenzhen, Shenzhen, China 4Division of Experimental Genetics, Sidra Medical and Research Center, Doha, Qatar 5Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia Genomics Facility, Selangor Darul Ehsan, Malaysia 6School of Science, Monash University Malaysia, Selangor Darul Ehsan, Malaysia 7The Third People’s Hospital of the TibetAutonomousRegion,Lhasa,China 8James D. Watson Institute of Genome Science, Hangzhou, China 9Department of Genetics & Genome Biology, University of Leicester, Leicester, United Kingdom 10Institute of Linguistics, University of Bern, Bern, Switzerland †These authors contributed equally to this work. *Corresponding authors: E-mails: [email protected]; [email protected]. Associate editor: Rasmus Nielsen Abstract We genotyped 738 individuals belonging to 49 populations from Nepal, Bhutan, North India, or Tibet at over 500,000 SNPs, and analyzed the genotypes in the context of available worldwide population data in order to investigate the demographic history of the region and the genetic adaptations to the harsh environment.
    [Show full text]