Structural Features of Tight-Junction Proteins
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Murine MPDZ-Linked Hydrocephalus Is Caused by Hyperpermeability of the Choroid Plexus
Thomas Jefferson University Jefferson Digital Commons Cardeza Foundation for Hematologic Research Sidney Kimmel Medical College 1-1-2019 Murine MPDZ-linked hydrocephalus is caused by hyperpermeability of the choroid plexus. Junning Yang Thomas Jefferson University Claire Simonneau Thomas Jefferson University Robert Kilker Thomas Jefferson University Laura Oakley ThomasFollow this Jeff anderson additional University works at: https://jdc.jefferson.edu/cardeza_foundation Part of the Medical Pathology Commons Matthew D. Byrne ThomasLet us Jeff knowerson Univ howersity access to this document benefits ouy Recommended Citation See next page for additional authors Yang, Junning; Simonneau, Claire; Kilker, Robert; Oakley, Laura; Byrne, Matthew D.; Nichtova, Zuzana; Stefanescu, Ioana; Pardeep-Kumar, Fnu; Tripathi, Sushil; Londin, Eric; Saugier-Veber, Pascale; Willard, Belinda; Thakur, Mathew; Pickup, Stephen; Ishikawa, Hiroshi; Schroten, Horst; Smeyne, Richard; and Horowitz, Arie, "Murine MPDZ-linked hydrocephalus is caused by hyperpermeability of the choroid plexus." (2019). Cardeza Foundation for Hematologic Research. Paper 49. https://jdc.jefferson.edu/cardeza_foundation/49 This Article is brought to you for free and open access by the Jefferson Digital Commons. The Jefferson Digital Commons is a service of Thomas Jefferson University's Center for Teaching and Learning (CTL). The Commons is a showcase for Jefferson books and journals, peer-reviewed scholarly publications, unique historical collections from the University archives, and teaching tools. The Jefferson Digital Commons allows researchers and interested readers anywhere in the world to learn about and keep up to date with Jefferson scholarship. This article has been accepted for inclusion in Cardeza Foundation for Hematologic Research by an authorized administrator of the Jefferson Digital Commons. For more information, please contact: [email protected]. -
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. -
Network Assessment of Demethylation Treatment in Melanoma: Differential Transcriptome-Methylome and Antigen Profile Signatures
RESEARCH ARTICLE Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures Zhijie Jiang1☯, Caterina Cinti2☯, Monia Taranta2, Elisabetta Mattioli3,4, Elisa Schena3,5, Sakshi Singh2, Rimpi Khurana1, Giovanna Lattanzi3,4, Nicholas F. Tsinoremas1,6, 1 Enrico CapobiancoID * a1111111111 1 Center for Computational Science, University of Miami, Miami, FL, United States of America, 2 Institute of Clinical Physiology, CNR, Siena, Italy, 3 CNR Institute of Molecular Genetics, Bologna, Italy, 4 IRCCS Rizzoli a1111111111 Orthopedic Institute, Bologna, Italy, 5 Endocrinology Unit, Department of Medical & Surgical Sciences, Alma a1111111111 Mater Studiorum University of Bologna, S Orsola-Malpighi Hospital, Bologna, Italy, 6 Department of a1111111111 Medicine, University of Miami, Miami, FL, United States of America a1111111111 ☯ These authors contributed equally to this work. * [email protected] OPEN ACCESS Abstract Citation: Jiang Z, Cinti C, Taranta M, Mattioli E, Schena E, Singh S, et al. (2018) Network assessment of demethylation treatment in Background melanoma: Differential transcriptome-methylome and antigen profile signatures. PLoS ONE 13(11): In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the met- e0206686. https://doi.org/10.1371/journal. astatic process. These effects are usually measured by changes in both methylome and pone.0206686 transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at Editor: Roger Chammas, Universidade de Sao systems scale the significance of epigenetic treatment in melanoma cells with different met- Paulo, BRAZIL astatic potential. Received: June 20, 2018 Accepted: October 17, 2018 Methods and findings Published: November 28, 2018 Treatment by DAC demethylation with 5-Aza-2'-deoxycytidine of two melanoma cell lines Copyright: © 2018 Jiang et al. -
MUC4/MUC16/Muc20high Signature As a Marker of Poor Prognostic for Pancreatic, Colon and Stomach Cancers
Jonckheere and Van Seuningen J Transl Med (2018) 16:259 https://doi.org/10.1186/s12967-018-1632-2 Journal of Translational Medicine RESEARCH Open Access Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large‑scale genomic databases: MUC4/MUC16/ MUC20 signature is associated with poor survival in human carcinomas Nicolas Jonckheere* and Isabelle Van Seuningen* Abstract Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). Results: We identifed 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signal- ing. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with diferent localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adeno- carcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically signifcant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer. -
Not Just Another Scaffolding Protein Family: the Multifaceted Mpps
molecules Review Not Just Another Scaffolding Protein Family: The Multifaceted MPPs 1, 1, 1 Agnieszka Chytła y , Weronika Gajdzik-Nowak y , Paulina Olszewska , Agnieszka Biernatowska 1 , Aleksander F. Sikorski 2 and Aleksander Czogalla 1,* 1 Department of Cytobiochemistry, Faculty of Biotechnology, University of Wroclaw, 50-383 Wroclaw, Poland; [email protected] (A.C.); [email protected] (W.G.-N.); [email protected] (P.O.); [email protected] (A.B.) 2 Research and Development Center, Regional Specialist Hospital, Kamie´nskiego73a, 51-154 Wroclaw, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-71375-6356 These authors contribute equally. y Academic Editor: Luís M.S. Loura Received: 16 September 2020; Accepted: 20 October 2020; Published: 26 October 2020 Abstract: Membrane palmitoylated proteins (MPPs) are a subfamily of a larger group of multidomain proteins, namely, membrane-associated guanylate kinases (MAGUKs). The ubiquitous expression and multidomain structure of MPPs provide the ability to form diverse protein complexes at the cell membranes, which are involved in a wide range of cellular processes, including establishing the proper cell structure, polarity and cell adhesion. The formation of MPP-dependent complexes in various cell types seems to be based on similar principles, but involves members of different protein groups, such as 4.1-ezrin-radixin-moesin (FERM) domain-containing proteins, polarity proteins or other MAGUKs, showing their multifaceted nature. In this review, we discuss the function of the MPP family in the formation of multiple protein complexes. Notably, we depict their significant role for cell physiology, as the loss of interactions between proteins involved in the complex has a variety of negative consequences. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
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. -
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, -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
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. -
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 -
Cell Biology of Tight Junction Barrier Regulation and Mucosal Disease
Downloaded from http://cshperspectives.cshlp.org/ on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Cell Biology of Tight Junction Barrier Regulation and Mucosal Disease Aaron Buckley and Jerrold R. Turner Departments of Pathology and Medicine (Gastroenterology), Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115 Correspondence: [email protected] Mucosal surfaces are lined by epithelial cells. In the intestine, the epithelium establishes a selectively permeable barrier that supports nutrient absorption and waste secretion while preventing intrusion by luminal materials. Intestinal epithelia therefore play a central role in regulating interactions between the mucosal immune system and luminal contents, which include dietary antigens, a diverse intestinal microbiome, and pathogens. The paracellular space is sealed by the tight junction, which is maintained by a complex network of protein interactions. Tight junction dysfunction has been linked to a variety of local and systemic diseases. Two molecularly and biophysically distinct pathways across the intestinal tight junc- tion are selectively and differentially regulated by inflammatory stimuli. This review discusses the mechanisms underlying these events, their impact on disease, and the potential of using these as paradigms for development of tight junction-targeted therapeutic interventions. ucosal surfaces and the epithelial cells that adherens). The tight junction is a selectively Mline them are present at sites where tissues permeable barrier that generally represents the interface directly with the external environment rate-limiting step of paracellular transport. The or internal compartments that are contiguous adherens junction and desmosome provide es- with the external environment. Examples in- sential adhesive and mechanical properties that clude the gastrointestinal tract, the pulmonary contribute to barrier function but do not seal tree, and the genitourinary tract.