Kinase Fusions Are Frequent in Spitz Tumours and Spitzoid Melanomas
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Targeted Genes and Methodology Details for Neuromuscular Genetic Panels
Targeted Genes and Methodology Details for Neuromuscular Genetic Panels Reference transcripts based on build GRCh37 (hg19) interrogated by Neuromuscular Genetic Panels Next-generation sequencing (NGS) and/or Sanger sequencing is performed Motor Neuron Disease Panel to test for the presence of a mutation in these genes. Gene GenBank Accession Number Regions of homology, high GC-rich content, and repetitive sequences may ALS2 NM_020919 not provide accurate sequence. Therefore, all reported alterations detected ANG NM_001145 by NGS are confirmed by an independent reference method based on laboratory developed criteria. However, this does not rule out the possibility CHMP2B NM_014043 of a false-negative result in these regions. ERBB4 NM_005235 Sanger sequencing is used to confirm alterations detected by NGS when FIG4 NM_014845 appropriate.(Unpublished Mayo method) FUS NM_004960 HNRNPA1 NM_031157 OPTN NM_021980 PFN1 NM_005022 SETX NM_015046 SIGMAR1 NM_005866 SOD1 NM_000454 SQSTM1 NM_003900 TARDBP NM_007375 UBQLN2 NM_013444 VAPB NM_004738 VCP NM_007126 ©2018 Mayo Foundation for Medical Education and Research Page 1 of 14 MC4091-83rev1018 Muscular Dystrophy Panel Muscular Dystrophy Panel Gene GenBank Accession Number Gene GenBank Accession Number ACTA1 NM_001100 LMNA NM_170707 ANO5 NM_213599 LPIN1 NM_145693 B3GALNT2 NM_152490 MATR3 NM_199189 B4GAT1 NM_006876 MYH2 NM_017534 BAG3 NM_004281 MYH7 NM_000257 BIN1 NM_139343 MYOT NM_006790 BVES NM_007073 NEB NM_004543 CAPN3 NM_000070 PLEC NM_000445 CAV3 NM_033337 POMGNT1 NM_017739 CAVIN1 NM_012232 POMGNT2 -
Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Influencers on Thyroid Cancer Onset: Molecular Genetic Basis
G C A T T A C G G C A T genes Review Influencers on Thyroid Cancer Onset: Molecular Genetic Basis Berta Luzón-Toro 1,2, Raquel María Fernández 1,2, Leticia Villalba-Benito 1,2, Ana Torroglosa 1,2, Guillermo Antiñolo 1,2 and Salud Borrego 1,2,* 1 Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain; [email protected] (B.L.-T.); [email protected] (R.M.F.); [email protected] (L.V.-B.); [email protected] (A.T.); [email protected] (G.A.) 2 Centre for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain * Correspondence: [email protected]; Tel.: +34-955-012641 Received: 3 September 2019; Accepted: 6 November 2019; Published: 8 November 2019 Abstract: Thyroid cancer, a cancerous tumor or growth located within the thyroid gland, is the most common endocrine cancer. It is one of the few cancers whereby incidence rates have increased in recent years. It occurs in all age groups, from children through to seniors. Most studies are focused on dissecting its genetic basis, since our current knowledge of the genetic background of the different forms of thyroid cancer is far from complete, which poses a challenge for diagnosis and prognosis of the disease. In this review, we describe prevailing advances and update our understanding of the molecular genetics of thyroid cancer, focusing on the main genes related with the pathology, including the different noncoding RNAs associated with the disease. -
Synergistic Genetic Interactions Between Pkhd1 and Pkd1 Result in an ARPKD-Like Phenotype in Murine Models
BASIC RESEARCH www.jasn.org Synergistic Genetic Interactions between Pkhd1 and Pkd1 Result in an ARPKD-Like Phenotype in Murine Models Rory J. Olson,1 Katharina Hopp ,2 Harrison Wells,3 Jessica M. Smith,3 Jessica Furtado,1,4 Megan M. Constans,3 Diana L. Escobar,3 Aron M. Geurts,5 Vicente E. Torres,3 and Peter C. Harris 1,3 Due to the number of contributing authors, the affiliations are listed at the end of this article. ABSTRACT Background Autosomal recessive polycystic kidney disease (ARPKD) and autosomal dominant polycystic kidney disease (ADPKD) are genetically distinct, with ADPKD usually caused by the genes PKD1 or PKD2 (encoding polycystin-1 and polycystin-2, respectively) and ARPKD caused by PKHD1 (encoding fibrocys- tin/polyductin [FPC]). Primary cilia have been considered central to PKD pathogenesis due to protein localization and common cystic phenotypes in syndromic ciliopathies, but their relevance is questioned in the simple PKDs. ARPKD’s mild phenotype in murine models versus in humans has hampered investi- gating its pathogenesis. Methods To study the interaction between Pkhd1 and Pkd1, including dosage effects on the phenotype, we generated digenic mouse and rat models and characterized and compared digenic, monogenic, and wild-type phenotypes. Results The genetic interaction was synergistic in both species, with digenic animals exhibiting pheno- types of rapidly progressive PKD and early lethality resembling classic ARPKD. Genetic interaction be- tween Pkhd1 and Pkd1 depended on dosage in the digenic murine models, with no significant enhancement of the monogenic phenotype until a threshold of reduced expression at the second locus was breached. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
Genotyping of Breech Flystrike Resource – Update
Project No: ON-00515 Contract No: PO4500010753 AWI Project Manager: Bridget Peachey Contractor Name: CSIRO Agriculture and Food Prepared by: Dr Sonja Dominik Publication Date: July 2019 Genotyping of breech flystrike resource – update Published by Australian Wool Innovation Limited, Level 6, 68 Harrington Street, THE ROCKS, NSW, 2000 This publication should only be used as a general aid and is not a substitute for specific advice. To the extent permitted by law, we exclude all liability for loss or damage arising from the use of the information in this publication. AWI invests in research, development, innovation and marketing activities along the global supply chain for Australian wool. AWI is grateful for its funding, which is primarily provided by Australian woolgrowers through a wool levy and by the Australian Government which provides a matching contribution for eligible R&D activities © 2019 Australian Wool Innovation Ltd. All rights reserved. Contents Executive Summary .................................................................................................................... 3 1 Introduction/Hypothesis .................................................................................................... 5 2 Literature Review ............................................................................................................... 6 3 Project Objectives .............................................................................................................. 8 4 Success in Achieving Objectives ........................................................................................ -
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. -
RET Gene Fusions in Malignancies of the Thyroid and Other Tissues
G C A T T A C G G C A T genes Review RET Gene Fusions in Malignancies of the Thyroid and Other Tissues Massimo Santoro 1,*, Marialuisa Moccia 1, Giorgia Federico 1 and Francesca Carlomagno 1,2 1 Department of Molecular Medicine and Medical Biotechnology, University of Naples “Federico II”, 80131 Naples, Italy; [email protected] (M.M.); [email protected] (G.F.); [email protected] (F.C.) 2 Institute of Endocrinology and Experimental Oncology of the CNR, 80131 Naples, Italy * Correspondence: [email protected] Received: 10 March 2020; Accepted: 12 April 2020; Published: 15 April 2020 Abstract: Following the identification of the BCR-ABL1 (Breakpoint Cluster Region-ABelson murine Leukemia) fusion in chronic myelogenous leukemia, gene fusions generating chimeric oncoproteins have been recognized as common genomic structural variations in human malignancies. This is, in particular, a frequent mechanism in the oncogenic conversion of protein kinases. Gene fusion was the first mechanism identified for the oncogenic activation of the receptor tyrosine kinase RET (REarranged during Transfection), initially discovered in papillary thyroid carcinoma (PTC). More recently, the advent of highly sensitive massive parallel (next generation sequencing, NGS) sequencing of tumor DNA or cell-free (cfDNA) circulating tumor DNA, allowed for the detection of RET fusions in many other solid and hematopoietic malignancies. This review summarizes the role of RET fusions in the pathogenesis of human cancer. Keywords: kinase; tyrosine kinase inhibitor; targeted therapy; thyroid cancer 1. The RET Receptor RET (REarranged during Transfection) was initially isolated as a rearranged oncoprotein upon the transfection of a human lymphoma DNA [1]. -
Kinase Inhibitors: an Introduction
David Peters Baran Group Meeting Kinase Inhibitors: An Introduction 2/2/19 INTRODUCTION SIGNAL TRANSDUCTION KINASES ARE IMPORTANT IN HUMAN BIOLOGY/DISEASE: The process describing how a signal (chemical/physical) is transmitted through a - Kinases are a superfamily of proteins (5th largest in humans) cell ultimately resulting in a cellular response - 518 genes and 106 pseudogenes - Diverse in size, subunit structure, and cellular location RECEPTOR TRANSDUCERS EFFECTORS - ~260 residues make up their conserved catalytic core - dysregulation of kinases occurs in many diseases (cancer, inflamatory, degenerative, and autoimmune diseases) - signals can originate inside or outside the cell - 244 of the 518 map to disease loci - signals generally passed through a series of steps (signal transduction pathway) often consisting of multiple enzymes and messengers protein O - pathways open possibility for signal aplification (>1x106) kinase ATP + PROTEIN OH ADP + PROTEIN O P O - Extracellular signals transduced by RTKs, GPCR’s, guanlyl cyclases, or ligand-gated Ion channels O - Phosphorylation is the most prominent covalent modification/signal in KINASES INHIBITORS ARE IMPORTANT IN DISEASE THERAPY/RESEARCH: cellular regulation - currently 51 FDA approved small molecule kinase inhibitors - “converter enzymes” (protein kinases and phosphoprotein phosphorlyases) - 4 FDA approved antibody kinase inhibitors are regulated; conserve ATP/maintain desired target protein state - thousands of known inhibitors spanning the kinome - pathway ends by affecting biomolecule -
Clinical Activity of Ceritinib in ROS1-Rearranged Non-Small Cell Lung Cancer: Bench to Bedside Report LETTER Vivek Subbiaha,1, David S
LETTER Clinical activity of ceritinib in ROS1-rearranged non-small cell lung cancer: Bench to bedside report LETTER Vivek Subbiaha,1, David S. Honga, and Funda Meric-Bernstama We read with great interest the article by Davare et al. (1) profiling revealed a CD72-ROS1 rearrangement. The on structural insight of ROS1 tyrosine kinase inhibitors. patient was started on crizotinib at 250 mg orally twice Non-small cell lung cancer (NSCLC) is no longer a single daily and exhibited resolution of metastatic disease. disease but a collection of genetically heterogeneous The patient remained disease-free for 13 mo when tumors with different therapeutic options [e.g., aberra- CT-scan showed a relapse with two nodules in the tions in EGFR, BRAF, HER2/Neu, RET, anaplastic lym- right lower lobe. He underwent stereotactic radiation phoma kinase (ALK), and ROS1-rearranged tumors]. therapy. Imaging showed a treatment response but Each of these options has different clinical characteristics new pleural nodules. MRI brain scan showed new in- and therapeutic options with agents that have varying tracranial metastases. After undergoing gamma knife degrees of systemic activity. ROS1 gene rearrange- radiosurgery, the patient was enrolled in an ipilimumab ments define a distinct molecular subgroup of NSCLC. and radiation trial (NCT02239900). His disease pro- ROS1 rearrangement leads to constitutive ROS1 activa- gressed on ipilimumab. The patient was next enrolled tion and activity of crizotinib against ROS1-rearranged on the “Signature Trial,” a modular phase II study to link NSCLC was noted and crizotinib received regulatory targeted therapy to patients with pathway activated tu- approval for the treatment of ROS1 rearranged NSCLC mors; in this study patients whose tumors have aberra- (2). -
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