Interpretation of Genomic Sequencing Results in Healthy and Ill Newborns: Results from the Babyseq Project
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The Diversity of Dolichol-Linked Precursors to Asn-Linked Glycans Likely Results from Secondary Loss of Sets of Glycosyltransferases
The diversity of dolichol-linked precursors to Asn-linked glycans likely results from secondary loss of sets of glycosyltransferases John Samuelson*†, Sulagna Banerjee*, Paula Magnelli*, Jike Cui*, Daniel J. Kelleher‡, Reid Gilmore‡, and Phillips W. Robbins* *Department of Molecular and Cell Biology, Boston University Goldman School of Dental Medicine, 715 Albany Street, Boston, MA 02118-2932; and ‡Department of Biochemistry and Molecular Biology, University of Massachusetts Medical School, Worcester, MA 01665-0103 Contributed by Phillips W. Robbins, December 17, 2004 The vast majority of eukaryotes (fungi, plants, animals, slime mold, to N-glycans of improperly folded proteins, which are retained in and euglena) synthesize Asn-linked glycans (Alg) by means of a the ER by conserved glucose-binding lectins (calnexin͞calreticulin) lipid-linked precursor dolichol-PP-GlcNAc2Man9Glc3. Knowledge of (13). Although the Alg glycosyltransferases in the lumen of ER this pathway is important because defects in the glycosyltrans- appear to be eukaryote-specific, archaea and Campylobacter sp. ferases (Alg1–Alg12 and others not yet identified), which make glycosylate the sequon Asn and͞or contain glycosyltransferases dolichol-PP-glycans, lead to numerous congenital disorders of with domains like those of Alg1, Alg2, Alg7, and STT3 (1, 14–16). glycosylation. Here we used bioinformatic and experimental Protists, unicellular eukaryotes, suggest three notable exceptions methods to characterize Alg glycosyltransferases and dolichol- to the N-linked glycosylation path described in yeast and animals PP-glycans of diverse protists, including many human patho- (17). First, the kinetoplastid Trypanosoma cruzi (cause of Chagas gens, with the following major conclusions. First, it is demon- myocarditis), fails to glucosylate the dolichol-PP-linked precursor strated that common ancestry is a useful method of predicting and so makes dolichol-PP-GlcNAc2Man9 (18). -
Ovulation-Selective Genes: the Generation and Characterization of an Ovulatory-Selective Cdna Library
531 Ovulation-selective genes: the generation and characterization of an ovulatory-selective cDNA library A Hourvitz1,2*, E Gershon2*, J D Hennebold1, S Elizur2, E Maman2, C Brendle1, E Y Adashi1 and N Dekel2 1Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah 84132, USA 2Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel (Requests for offprints should be addressed to N Dekel; Email: [email protected]) *(A Hourvitz and E Gershon contributed equally to this paper) (J D Hennebold is now at Division of Reproductive Sciences, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, Oregon 97006, USA) Abstract Ovulation-selective/specific genes, that is, genes prefer- (FAE-1) homolog, found to be localized to the inner entially or exclusively expressed during the ovulatory periantral granulosa and to the cumulus granulosa cells of process, have been the subject of growing interest. We antral follicles. The FAE-1 gene is a -ketoacyl-CoA report herein studies on the use of suppression subtractive synthase belonging to the fatty acid elongase (ELO) hybridization (SSH) to construct a ‘forward’ ovulation- family, which catalyzes the initial step of very long-chain selective/specific cDNA library. In toto, 485 clones were fatty acid synthesis. All in all, the present study accom- sequenced and analyzed for homology to known genes plished systematic identification of those hormonally with the basic local alignment tool (BLAST). Of those, regulated genes that are expressed in the ovary in an 252 were determined to be nonredundant. -
CADASIL Testing
Lab Management Guidelines V1.0.2020 CADASIL Testing MOL.TS.144.A v1.0.2020 Introduction CADASIL testing is addressed by this guideline. Procedures addressed The inclusion of any procedure code in this table does not imply that the code is under management or requires prior authorization. Refer to the specific Health Plan's procedure code list for management requirements. Procedures addressed by this Procedure codes guideline NOTCH3 Known Familial Mutation 81403 Analysis NOTCH3 Targeted Sequencing 81406 NOTCH3 Deletion/Duplication Analysis 81479 What is CADASIL Definition CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy) is an adult-onset form of cerebrovascular disease. There are no generally accepted clinical diagnostic criteria for CADASIL and symptoms vary among affected individuals. Signs and symptoms Typical signs and symptoms include1,2,3 Transient ischemic attacks and ischemic stroke, occurs at a mean age of 47 years (age range 20-70 years), in most cases without conventional vascular risk factors cognitive disturbance, primarily affecting executive function, may start as early as age 35 years psychiatric or behavioral abnormalities migraine with aura, occurs with a mean age of onset of 30 years (age range 6-48 years), and Less common symptoms include: © 2020 eviCore healthcare. All Rights Reserved. 1 of 7 400 Buckwalter Place Boulevard, Bluffton, SC 29910 (800) 918-8924 www.eviCore.com Lab Management Guidelines V1.0.2020 recurrent seizures with onset in middle age, usually secondary to stroke acute encephalopathy, with a mean age of onset of 42 years Life expectancy for men with CADASIL is reduced by approximately five years and for women by 1 to 2 years.4 Diagnosis Brain Magnetic Resonance Imaging (MRI) findings include T2-signal-abnormalities in the white matter of the temporal pole and T2-signal-abnormalities in the external capsule and corpus callosum.1,2 CADASIL is suspected in an individual with the clinical signs and MRI findings. -
Mapping Influenza-Induced Posttranslational Modifications On
viruses Article Mapping Influenza-Induced Posttranslational Modifications on Histones from CD8+ T Cells Svetlana Rezinciuc 1, Zhixin Tian 2, Si Wu 2, Shawna Hengel 2, Ljiljana Pasa-Tolic 2 and Heather S. Smallwood 1,3,* 1 Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA; [email protected] 2 Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA; [email protected] (Z.T.); [email protected] (S.W.); [email protected] (S.H.); [email protected] (L.P.-T.) 3 Children’s Foundation Research Institute, Memphis, TN 38105, USA * Correspondence: [email protected]; Tel.: +1-(901)-448–3068 Academic Editor: Italo Tempera Received: 10 October 2020; Accepted: 2 December 2020; Published: 8 December 2020 Abstract: T cell function is determined by transcriptional networks that are regulated by epigenetic programming via posttranslational modifications (PTMs) to histone proteins and DNA. Bottom-up mass spectrometry (MS) can identify histone PTMs, whereas intact protein analysis by MS can detect species missed by bottom-up approaches. We used a novel approach of online two-dimensional liquid chromatography-tandem MS with high-resolution reversed-phase liquid chromatography (RPLC), alternating electron transfer dissociation (ETD) and collision-induced dissociation (CID) on precursor ions to maximize fragmentation of uniquely modified species. The first online RPLC separation sorted histone families, then RPLC or weak cation exchange hydrophilic interaction liquid chromatography (WCX-HILIC) separated species heavily clad in PTMs. Tentative identifications were assigned by matching proteoform masses to predicted theoretical masses that were verified with tandem MS. We used this innovative approach for histone-intact protein PTM mapping (HiPTMap) to identify and quantify proteoforms purified from CD8 T cells after in vivo influenza infection. -
CRISPR Screening of Porcine Sgrna Library Identifies Host Factors
ARTICLE https://doi.org/10.1038/s41467-020-18936-1 OPEN CRISPR screening of porcine sgRNA library identifies host factors associated with Japanese encephalitis virus replication Changzhi Zhao1,5, Hailong Liu1,5, Tianhe Xiao1,5, Zichang Wang1, Xiongwei Nie1, Xinyun Li1,2, Ping Qian2,3, Liuxing Qin3, Xiaosong Han1, Jinfu Zhang1, Jinxue Ruan1, Mengjin Zhu1,2, Yi-Liang Miao 1,2, Bo Zuo1,2, ✉ ✉ Kui Yang4, Shengsong Xie 1,2 & Shuhong Zhao 1,2 1234567890():,; Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that causes ence- phalitis and reproductive disorders in mammalian species. However, the host factors critical for its entry, replication, and assembly are poorly understood. Here, we design a porcine genome-scale CRISPR/Cas9 knockout (PigGeCKO) library containing 85,674 single guide RNAs targeting 17,743 protein-coding genes, 11,053 long ncRNAs, and 551 microRNAs. Subsequently, we use the PigGeCKO library to identify key host factors facilitating JEV infection in porcine cells. Several previously unreported genes required for JEV infection are highly enriched post-JEV selection. We conduct follow-up studies to verify the dependency of JEV on these genes, and identify functional contributions for six of the many candidate JEV- related host genes, including EMC3 and CALR. Additionally, we identify that four genes associated with heparan sulfate proteoglycans (HSPGs) metabolism, specifically those responsible for HSPGs sulfurylation, facilitate JEV entry into porcine cells. Thus, beyond our development of the largest CRISPR-based functional genomic screening platform for pig research to date, this study identifies multiple potentially vulnerable targets for the devel- opment of medical and breeding technologies to treat and prevent diseases caused by JEV. -
The National Economic Burden of Rare Disease Study February 2021
Acknowledgements This study was sponsored by the EveryLife Foundation for Rare Diseases and made possible through the collaborative efforts of the national rare disease community and key stakeholders. The EveryLife Foundation thanks all those who shared their expertise and insights to provide invaluable input to the study including: the Lewin Group, the EveryLife Community Congress membership, the Technical Advisory Group for this study, leadership from the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH), the Undiagnosed Diseases Network (UDN), the Little Hercules Foundation, the Rare Disease Legislative Advocates (RDLA) Advisory Committee, SmithSolve, and our study funders. Most especially, we thank the members of our rare disease patient and caregiver community who participated in this effort and have helped to transform their lived experience into quantifiable data. LEWIN GROUP PROJECT STAFF Grace Yang, MPA, MA, Vice President Inna Cintina, PhD, Senior Consultant Matt Zhou, BS, Research Consultant Daniel Emont, MPH, Research Consultant Janice Lin, BS, Consultant Samuel Kallman, BA, BS, Research Consultant EVERYLIFE FOUNDATION PROJECT STAFF Annie Kennedy, BS, Chief of Policy and Advocacy Julia Jenkins, BA, Executive Director Jamie Sullivan, MPH, Director of Policy TECHNICAL ADVISORY GROUP Annie Kennedy, BS, Chief of Policy & Advocacy, EveryLife Foundation for Rare Diseases Anne Pariser, MD, Director, Office of Rare Diseases Research, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health Elisabeth M. Oehrlein, PhD, MS, Senior Director, Research and Programs, National Health Council Christina Hartman, Senior Director of Advocacy, The Assistance Fund Kathleen Stratton, National Academies of Science, Engineering and Medicine (NASEM) Steve Silvestri, Director, Government Affairs, Neurocrine Biosciences Inc. -
Counsyl Foresight™ Carrier Screen Disease List
COUNSYL FORESIGHT™ CARRIER SCREEN DISEASE LIST The Counsyl Foresight Carrier Screen focuses on serious, clinically-actionable, and prevalent conditions to ensure you are providing meaningful information to your patients. 11-Beta-Hydroxylase- Bardet-Biedl Syndrome, Congenital Disorder of Galactokinase Deficiency Deficient Congenital Adrenal BBS1-Related (BBS1) Glycosylation, Type Ic (ALG6) (GALK1) Hyperplasia (CYP11B1) Bardet-Biedl Syndrome, Congenital Finnish Nephrosis Galactosemia (GALT ) 21-Hydroxylase-Deficient BBS10-Related (BBS10) (NPHS1) Gamma-Sarcoglycanopathy Congenital Adrenal Bardet-Biedl Syndrome, Costeff Optic Atrophy (SGCG) Hyperplasia (CYP21A2)* BBS12-Related (BBS12) Syndrome (OPA3) Gaucher Disease (GBA)* 6-Pyruvoyl-Tetrahydropterin Bardet-Biedl Syndrome, Cystic Fibrosis GJB2-Related DFNB1 Synthase Deficiency (PTS) BBS2-Related (BBS2) (CFTR) Nonsyndromic Hearing Loss ABCC8-Related Beta-Sarcoglycanopathy and Deafness (including two Cystinosis (CTNS) Hyperinsulinism (ABCC8) (including Limb-Girdle GJB6 deletions) (GJB2) Muscular Dystrophy, Type 2E) D-Bifunctional Protein Adenosine Deaminase GLB1-Related Disorders (SGCB) Deficiency (HSD17B4) Deficiency (ADA) (GLB1) Biotinidase Deficiency (BTD) Delta-Sarcoglycanopathy Adrenoleukodystrophy: GLDC-Related Glycine (SGCD) X-Linked (ABCD1) Bloom Syndrome (BLM) Encephalopathy (GLDC) Alpha Thalassemia (HBA1/ Calpainopathy (CAPN3) Dysferlinopathy (DYSF) Glutaric Acidemia, Type 1 HBA2)* Canavan Disease Dystrophinopathies (including (GCDH) (ASPA) Alpha-Mannosidosis Duchenne/Becker Muscular -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
Child Neurology: Hereditary Spastic Paraplegia in Children S.T
RESIDENT & FELLOW SECTION Child Neurology: Section Editor Hereditary spastic paraplegia in children Mitchell S.V. Elkind, MD, MS S.T. de Bot, MD Because the medical literature on hereditary spastic clinical feature is progressive lower limb spasticity B.P.C. van de paraplegia (HSP) is dominated by descriptions of secondary to pyramidal tract dysfunction. HSP is Warrenburg, MD, adult case series, there is less emphasis on the genetic classified as pure if neurologic signs are limited to the PhD evaluation in suspected pediatric cases of HSP. The lower limbs (although urinary urgency and mild im- H.P.H. Kremer, differential diagnosis of progressive spastic paraplegia pairment of vibration perception in the distal lower MD, PhD strongly depends on the age at onset, as well as the ac- extremities may occur). In contrast, complicated M.A.A.P. Willemsen, companying clinical features, possible abnormalities on forms of HSP display additional neurologic and MRI abnormalities such as ataxia, more significant periph- MD, PhD MRI, and family history. In order to develop a rational eral neuropathy, mental retardation, or a thin corpus diagnostic strategy for pediatric HSP cases, we per- callosum. HSP may be inherited as an autosomal formed a literature search focusing on presenting signs Address correspondence and dominant, autosomal recessive, or X-linked disease. reprint requests to Dr. S.T. de and symptoms, age at onset, and genotype. We present Over 40 loci and nearly 20 genes have already been Bot, Radboud University a case of a young boy with a REEP1 (SPG31) mutation. Nijmegen Medical Centre, identified.1 Autosomal dominant transmission is ob- Department of Neurology, PO served in 70% to 80% of all cases and typically re- Box 9101, 6500 HB, Nijmegen, CASE REPORT A 4-year-old boy presented with 2 the Netherlands progressive walking difficulties from the time he sults in pure HSP. -
Broad and Thematic Remodeling of the Surface Glycoproteome on Isogenic
bioRxiv preprint doi: https://doi.org/10.1101/808139; this version posted October 17, 2019. 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. Broad and thematic remodeling of the surface glycoproteome on isogenic cells transformed with driving proliferative oncogenes Kevin K. Leung1,5, Gary M. Wilson2,5, Lisa L. Kirkemo1, Nicholas M. Riley2,4, Joshua J. Coon2,3, James A. Wells1* 1Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA, USA Departments of Chemistry2 and Biomolecular Chemistry3, University of Wisconsin- Madison, Madison, WI, 53706, USA 4Present address Department of Chemistry, Stanford University, Stanford, CA, 94305, USA 5These authors contributed equally *To whom correspondence should be addressed bioRxiv preprint doi: https://doi.org/10.1101/808139; this version posted October 17, 2019. 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. Abstract: The cell surface proteome, the surfaceome, is the interface for engaging the extracellular space in normal and cancer cells. Here We apply quantitative proteomics of N-linked glycoproteins to reveal how a collection of some 700 surface proteins is dramatically remodeled in an isogenic breast epithelial cell line stably expressing any of six of the most prominent proliferative oncogenes, including the receptor tyrosine kinases, EGFR and HER2, and downstream signaling partners such as KRAS, BRAF, MEK and AKT. -
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. -
Yeast Genome Gazetteer P35-65
gazetteer Metabolism 35 tRNA modification mitochondrial transport amino-acid metabolism other tRNA-transcription activities vesicular transport (Golgi network, etc.) nitrogen and sulphur metabolism mRNA synthesis peroxisomal transport nucleotide metabolism mRNA processing (splicing) vacuolar transport phosphate metabolism mRNA processing (5’-end, 3’-end processing extracellular transport carbohydrate metabolism and mRNA degradation) cellular import lipid, fatty-acid and sterol metabolism other mRNA-transcription activities other intracellular-transport activities biosynthesis of vitamins, cofactors and RNA transport prosthetic groups other transcription activities Cellular organization and biogenesis 54 ionic homeostasis organization and biogenesis of cell wall and Protein synthesis 48 plasma membrane Energy 40 ribosomal proteins organization and biogenesis of glycolysis translation (initiation,elongation and cytoskeleton gluconeogenesis termination) organization and biogenesis of endoplasmic pentose-phosphate pathway translational control reticulum and Golgi tricarboxylic-acid pathway tRNA synthetases organization and biogenesis of chromosome respiration other protein-synthesis activities structure fermentation mitochondrial organization and biogenesis metabolism of energy reserves (glycogen Protein destination 49 peroxisomal organization and biogenesis and trehalose) protein folding and stabilization endosomal organization and biogenesis other energy-generation activities protein targeting, sorting and translocation vacuolar and lysosomal