Claudins: New Players in Human Fertility and Reproductive System Cancers
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The Utility of Genetic Risk Scores in Predicting the Onset of Stroke March 2021 6
DOT/FAA/AM-21/24 Office of Aerospace Medicine Washington, DC 20591 The Utility of Genetic Risk Scores in Predicting the Onset of Stroke Diana Judith Monroy Rios, M.D1 and Scott J. Nicholson, Ph.D.2 1. KR 30 # 45-03 University Campus, Building 471, 5th Floor, Office 510 Bogotá D.C. Colombia 2. FAA Civil Aerospace Medical Institute, 6500 S. MacArthur Blvd Rm. 354, Oklahoma City, OK 73125 March 2021 NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for the contents thereof. _________________ This publication and all Office of Aerospace Medicine technical reports are available in full-text from the Civil Aerospace Medical Institute’s publications Web site: (www.faa.gov/go/oamtechreports) Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. DOT/FAA/AM-21/24 4. Title and Subtitle 5. Report Date March 2021 The Utility of Genetic Risk Scores in Predicting the Onset of Stroke 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Diana Judith Monroy Rios M.D1, and Scott J. Nicholson, Ph.D.2 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 1 KR 30 # 45-03 University Campus, Building 471, 5th Floor, Office 510, Bogotá D.C. Colombia 11. Contract or Grant No. 2 FAA Civil Aerospace Medical Institute, 6500 S. MacArthur Blvd Rm. 354, Oklahoma City, OK 73125 12. Sponsoring Agency name and Address 13. Type of Report and Period Covered Office of Aerospace Medicine Federal Aviation Administration 800 Independence Ave., S.W. -
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888.267.4436 [email protected] www.origene.com Name:CLDN6 mouse monoclonal antibody, clone OTI3E3 (formerly 3E3) Catalog: TA507011 Product Data Sheet - TRUEMAB Components: • CLDN6 mouse monoclonal antibody, clone OTI3E3 (formerly 3E3) (TA507011) • WB positive control also available: 20ug myc-DDK tagged CLDN6 HEK293T over-expression lysate lyophilized in RIPA buffer (LC412034). (Reconstitute into 20ul of 1x SDS sample buffer before loading; load 5ul per lane as WB control or as desired) Amount: 100ul Immunogen: Full length human recombinant protein of human CLDN6(NP_067018) produced in HEK293T cell. Host: Mouse Isotype: IgG1 Species Reactivity: Human Guaranteed WB, IF Applications: Suggested WB 1:1000, IF 1:100, Dilutions: Concentration: 1 mg/ml Buffer: PBS (PH 7.3) containing 1% BSA, 50% glycerol and 0.02% sodium azide. Purification: Purified from mouse ascites fluids by affinity chromatography Storage Condition: Shipped at -20C or with ice packs. Upon delivery store at -20C. Dilute in PBS (pH7.3) if necessary. Stable for 12 months from date of receipt. Avoid repeated freeze-thaws. Target Target Name: Homo sapiens claudin 6 (CLDN6) Alternative Name: OTTHUMP00000159248; claudin 6 Database Link: NP_067018 Entrez Gene 9074 Human Function: Tight junctions represent one mode of cell-to-cell adhesion in epithelial or endothelial cell sheets, forming continuous seals around cells and serving as a physical barrier to prevent solutes and water from passing freely through the paracellular space. These junctions are comprised of sets of continuous networking strands in the outwardly facing cytoplasmic leaflet, with complementary grooves in the inwardly facing extracytoplasmic leaflet. This gene encodes a component of tight junction strands, which This product is to be used for laboratory only. -
Claudin-2: Roles Beyond Permeability Functions
International Journal of Molecular Sciences Review Claudin-2: Roles beyond Permeability Functions Shruthi Venugopal, Shaista Anwer and Katalin Szászi * Keenan Research Centre for Biomedical Science of the St. Michael’s Hospital and Department of Surgery, University of Toronto, Toronto, ON M5B 1W8, Canada; [email protected] (S.V.); [email protected] (S.A.) * Correspondence: [email protected]; Tel.: +1-416-8471752 Received: 13 October 2019; Accepted: 9 November 2019; Published: 12 November 2019 Abstract: Claudin-2 is expressed in the tight junctions of leaky epithelia, where it forms cation-selective and water permeable paracellular channels. Its abundance is under fine control by a complex signaling network that affects both its synthesis and turnover in response to various environmental inputs. Claudin-2 expression is dysregulated in many pathologies including cancer, inflammation, and fibrosis. Claudin-2 has a key role in energy-efficient ion and water transport in the proximal tubules of the kidneys and in the gut. Importantly, strong evidence now also supports a role for this protein as a modulator of vital cellular events relevant to diseases. Signaling pathways that are overactivated in diseases can alter claudin-2 expression, and a good correlation exists between disease stage and claudin-2 abundance. Further, loss- and gain-of-function studies showed that primary changes in claudin-2 expression impact vital cellular processes such as proliferation, migration, and cell fate determination. These effects appear to be mediated by alterations in key signaling pathways. The specific mechanisms linking claudin-2 to these changes remain poorly understood, but adapters binding to the intracellular portion of claudin-2 may play a key role. -
Bioinformatics Analyses of Genomic Imprinting
Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________ -
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, -
Predicting Gene Ontology Biological Process from Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R
Methods Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R. Hvidsten,2 Herman Midelfart,2 Jan Komorowski,2,3,4 and Arne K. Sandvik1 1Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway; 2Department of Information and Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway; 3The Linnaeus Centre for Bioinformatics, Uppsala University, SE-751 24 Uppsala, Sweden The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end,supervised learning was used to analyz e microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition,new roles in biological process were hypothesi zed for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition,many biological process roles hypothesi zed for uncharacterized genes were found to agree with assumptions based on homology information. -
S41467-020-18249-3.Pdf
ARTICLE https://doi.org/10.1038/s41467-020-18249-3 OPEN Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain Lei Zhao1,2,17, Zhongqi Li 1,2,17, Joaquim S. L. Vong2,3,17, Xinyi Chen1,2, Hei-Ming Lai1,2,4,5,6, Leo Y. C. Yan1,2, Junzhe Huang1,2, Samuel K. H. Sy1,2,7, Xiaoyu Tian 8, Yu Huang 8, Ho Yin Edwin Chan5,9, Hon-Cheong So6,8, ✉ ✉ Wai-Lung Ng 10, Yamei Tang11, Wei-Jye Lin12,13, Vincent C. T. Mok1,5,6,14,15 &HoKo 1,2,4,5,6,8,14,16 1234567890():,; The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovas- cular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation- dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which pro- minently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood–brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed tran- scriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible. -
Methods in and Applications of the Sequencing of Short Non-Coding Rnas" (2013)
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Methods in and Applications of the Sequencing of Short Non- Coding RNAs Paul Ryvkin University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Molecular Biology Commons Recommended Citation Ryvkin, Paul, "Methods in and Applications of the Sequencing of Short Non-Coding RNAs" (2013). Publicly Accessible Penn Dissertations. 922. https://repository.upenn.edu/edissertations/922 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/922 For more information, please contact [email protected]. Methods in and Applications of the Sequencing of Short Non-Coding RNAs Abstract Short non-coding RNAs are important for all domains of life. With the advent of modern molecular biology their applicability to medicine has become apparent in settings ranging from diagonistic biomarkers to therapeutics and fields angingr from oncology to neurology. In addition, a critical, recent technological development is high-throughput sequencing of nucleic acids. The convergence of modern biotechnology with developments in RNA biology presents opportunities in both basic research and medical settings. Here I present two novel methods for leveraging high-throughput sequencing in the study of short non- coding RNAs, as well as a study in which they are applied to Alzheimer's Disease (AD). The computational methods presented here include High-throughput Annotation of Modified Ribonucleotides (HAMR), which enables researchers to detect post-transcriptional covalent modifications ot RNAs in a high-throughput manner. In addition, I describe Classification of RNAs by Analysis of Length (CoRAL), a computational method that allows researchers to characterize the pathways responsible for short non-coding RNA biogenesis. -
The Complex SNP and CNV Genetic Architecture of the Increased Risk of Congenital Heart Defects in Down Syndrome
Downloaded from genome.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press Research The complex SNP and CNV genetic architecture of the increased risk of congenital heart defects in Down syndrome M. Reza Sailani,1,2 Periklis Makrythanasis,1 Armand Valsesia,3,4,5 Federico A. Santoni,1 Samuel Deutsch,1 Konstantin Popadin,1 Christelle Borel,1 Eugenia Migliavacca,1 Andrew J. Sharp,1,20 Genevieve Duriaux Sail,1 Emilie Falconnet,1 Kelly Rabionet,6,7,8 Clara Serra-Juhe´,7,9 Stefano Vicari,10 Daniela Laux,11 Yann Grattau,12 Guy Dembour,13 Andre Megarbane,12,14 Renaud Touraine,15 Samantha Stora,12 Sofia Kitsiou,16 Helena Fryssira,16 Chariklia Chatzisevastou-Loukidou,16 Emmanouel Kanavakis,16 Giuseppe Merla,17 Damien Bonnet,11 Luis A. Pe´rez-Jurado,7,9 Xavier Estivill,6,7,8 Jean M. Delabar,18 and Stylianos E. Antonarakis1,2,19,21 1–19[Author affiliations appear at the end of the paper.] Congenital heart defect (CHD) occurs in 40% of Down syndrome (DS) cases. While carrying three copies of chromosome 21 increases the risk for CHD, trisomy 21 itself is not sufficient to cause CHD. Thus, additional genetic variation and/or environmental factors could contribute to the CHD risk. Here we report genomic variations that in concert with trisomy 21, determine the risk for CHD in DS. This case-control GWAS includes 187 DS with CHD (AVSD = 69, ASD = 53, VSD = 65) as cases, and 151 DS without CHD as controls. Chromosome 21–specific association studies revealed rs2832616 and rs1943950 as CHD risk alleles (adjusted genotypic P-values <0.05). -
Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT) Beyond SMARCA4 Mutations: a Comprehensive Genomic Analysis
cells Article Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT) beyond SMARCA4 Mutations: A Comprehensive Genomic Analysis Aurélie Auguste 1,Félix Blanc-Durand 2 , Marc Deloger 3 , Audrey Le Formal 1, Rohan Bareja 4,5, David C. Wilkes 4 , Catherine Richon 6,Béatrice Brunn 2, Olivier Caron 6, Mojgan Devouassoux-Shisheboran 7,Sébastien Gouy 2, Philippe Morice 2, Enrica Bentivegna 2, Andrea Sboner 4,5,8, Olivier Elemento 4,8, Mark A. Rubin 9 , Patricia Pautier 2, Catherine Genestie 10, Joanna Cyrta 4,9,11 and Alexandra Leary 1,2,* 1 Medical Oncologist, Gynecology Unit, Lead Translational Research Team, INSERM U981, Gustave Roussy, 94805 Villejuif, France; [email protected] (A.A.); [email protected] (A.L.F.) 2 Gynecological Unit, Department of Medicine, Gustave Roussy, 94805 Villejuif, France; [email protected] (F.B.-D.); [email protected] (B.B.); [email protected] (S.G.); [email protected] (P.M.); [email protected] (E.B.); [email protected] (P.P.) 3 Bioinformatics Core Facility, Gustave Roussy Cancer Center, UMS CNRS 3655/INSERM 23 AMMICA, 94805 Villejuif, France; [email protected] 4 Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10001, USA; [email protected] (R.B.); [email protected] (D.C.W.); [email protected] (A.S.); [email protected] (O.E.); [email protected] (J.C.) 5 Institute for Computational Biomedicine, Weill Cornell -
Computational Modeling of Claudin Structure and Function
International Journal of Molecular Sciences Review Computational Modeling of Claudin Structure and Function Shadi Fuladi 1, Ridaka-Wal Jannat 1 , Le Shen 2,3 , Christopher R. Weber 2,* and Fatemeh Khalili-Araghi 1,* 1 Department of Physics, University of Illinois at Chicago, Chicago, IL 60607, USA; [email protected] (S.F.); [email protected] (R.-W.J.) 2 Department of Pathology, University of Chicago, Chicago, IL 60637, USA; [email protected] 3 Department of Surgery, University of Chicago, Chicago, IL 60637, USA * Correspondence: [email protected] (C.R.W.); [email protected] (F.K.-A.) Received: 15 December 2019; Accepted: 16 January 2020; Published: 23 January 2020 Abstract: Tight junctions form a barrier to control passive transport of ions and small molecules across epithelia and endothelia. In addition to forming a barrier, some of claudins control transport properties of tight junctions by forming charge- and size-selective ion channels. It has been suggested claudin monomers can form or incorporate into tight junction strands to form channels. Resolving the crystallographic structure of several claudins in recent years has provided an opportunity to examine structural basis of claudins in tight junctions. Computational and theoretical modeling relying on atomic description of the pore have contributed significantly to our understanding of claudin pores and paracellular transport. In this paper, we review recent computational and mathematical modeling of claudin barrier function. We focus on dynamic modeling of global epithelial barrier function as a function of claudin pores and molecular dynamics studies of claudins leading to a functional model of claudin channels. Keywords: claudin; molecular dynamics; tight junction; ion transport; ion channel 1. -
Defenders and Challengers of Endothelial Barrier Function
REVIEW published: 18 December 2017 doi: 10.3389/fimmu.2017.01847 Defenders and Challengers of Endothelial Barrier Function Nader Rahimi* Department of Pathology, Boston University School of Medicine, Boston, MA, United States Regulated vascular permeability is an essential feature of normal physiology and its dysfunction is associated with major human diseases ranging from cancer to inflam- mation and ischemic heart diseases. Integrity of endothelial cells also play a prominent role in the outcome of surgical procedures and organ transplant. Endothelial barrier function and integrity are regulated by a plethora of highly specialized transmembrane receptors, including claudin family proteins, occludin, junctional adhesion molecules (JAMs), vascular endothelial (VE)-cadherin, and the newly identified immunoglobulin (Ig) and proline-rich receptor-1 (IGPR-1) through various distinct mechanisms and signaling. On the other hand, vascular endothelial growth factor (VEGF) and its tyrosine kinase receptor, VEGF receptor-2, play a central role in the destabilization of endothelial barrier function. While claudins and occludin regulate cell–cell junction via recruitment of zonula occludens (ZO), cadherins via catenin proteins, and JAMs via ZO and afadin, IGPR-1 recruits bullous pemphigoid antigen 1 [also called dystonin (DST) and SH3 protein inter- Edited by: acting with Nck90/WISH (SH3 protein interacting with Nck)]. Endothelial barrier function Thomas Luft, is moderated by the function of transmembrane receptors and signaling events that act University Hospital Heidelberg, Germany to defend or destabilize it. Here, I highlight recent advances that have provided new Reviewed by: insights into endothelial barrier function and mechanisms involved. Further investigation Luiza Guilherme, of these mechanisms could lead to the discovery of novel therapeutic targets for human University of São Paulo, Brazil diseases associated with endothelial dysfunction.