Target Gene Representative Transcript Gene Name 3P-Seq Tags + 5Total Sites8mer Sites 7Mer-M8 Sites PLA2G15 ENST00000566188.1
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
DNA Methylation of GHSR, GNG4, HOXD9 and SALL3 Is a Common Epigenetic Alteration in Thymic Carcinoma
INTERNATIONAL JOURNAL OF ONCOLOGY 56: 315-326, 2020 DNA methylation of GHSR, GNG4, HOXD9 and SALL3 is a common epigenetic alteration in thymic carcinoma REINA KISHIBUCHI1, KAZUYA KONDO1, SHIHO SOEJIMA1, MITSUHIRO TSUBOI2, KOICHIRO KAJIURA2, YUKIKIYO KAWAKAMI2, NAOYA KAWAKITA2, TORU SAWADA2, HIROAKI TOBA2, MITSUTERU YOSHIDA2, HIROMITSU TAKIZAWA2 and AKIRA TANGOKU2 1Department of Oncological Medical Services, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8509; 2Department of Thoracic, Endocrine Surgery and Oncology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan Received August 17, 2019; Accepted October 25, 2019 DOI: 10.3892/ijo.2019.4915 Abstract. Thymic epithelial tumors comprise thymoma, promoter methylation of the 4 genes was not significantly thymic carcinoma and neuroendocrine tumors of the thymus. higher in advanced-stage tumors than in early-stage tumors in Recent studies have revealed that the incidence of somatic all thymic epithelial tumors. Among the 4 genes, relapse-free non‑synonymous mutations is significantly higher in thymic survival was significantly worse in tumors with a higher DNA carcinoma than in thymoma. However, limited information methylation than in those with a lower DNA methylation in all is currently available on epigenetic alterations in these types thymic epithelial tumors. Moreover, relapse-free survival was of cancer. In this study, we thus performed genome-wide significantly worse in thymomas with a higher DNA methyla- screening of aberrantly methylated CpG islands in thymoma tion of HOXD9 and SALL3 than in those with a lower DNA and thymic carcinoma using Illumina HumanMethylation450 methylation. On the whole, the findings of this study indicated K BeadChip. We identified 92 CpG islands significantly that the promoter methylation of cancer-related genes was hypermethylated in thymic carcinoma in relation to thymoma significantly higher in thymic carcinoma than in thymoma and and selected G protein subunit gamma 4 (GNG4), growth the thymus. -
Down-Regulation of Stem Cell Genes, Including Those in a 200-Kb Gene Cluster at 12P13.31, Is Associated with in Vivo Differentiation of Human Male Germ Cell Tumors
Research Article Down-Regulation of Stem Cell Genes, Including Those in a 200-kb Gene Cluster at 12p13.31, Is Associated with In vivo Differentiation of Human Male Germ Cell Tumors James E. Korkola,1 Jane Houldsworth,1,2 Rajendrakumar S.V. Chadalavada,1 Adam B. Olshen,3 Debbie Dobrzynski,2 Victor E. Reuter,4 George J. Bosl,2 and R.S.K. Chaganti1,2 1Cell Biology Program and Departments of 2Medicine, 3Epidemiology and Biostatistics, and 4Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York Abstract on the degree and type of differentiation (i.e., seminomas, which Adult male germ cell tumors (GCTs) comprise distinct groups: resemble undifferentiated primitive germ cells, and nonseminomas, seminomas and nonseminomas, which include pluripotent which show varying degrees of embryonic and extraembryonic embryonal carcinomas as well as other histologic subtypes patterns of differentiation; refs. 2, 3). Nonseminomatous GCTs are exhibiting various stages of differentiation. Almost all GCTs further subdivided into embryonal carcinomas, which show early show 12p gain, but the target genes have not been clearly zygotic or embryonal-like differentiation, yolk sac tumors and defined. To identify 12p target genes, we examined Affymetrix choriocarcinomas, which exhibit extraembryonal forms of differ- (Santa Clara, CA) U133A+B microarray (f83% coverage of 12p entiation, and teratomas, which show somatic differentiation along genes) expression profiles of 17 seminomas, 84 nonseminoma multiple lineages (3). Both seminomas and embryonal carcinoma GCTs, and 5 normal testis samples. Seventy-three genes on 12p are known to express stem cell markers, such as POU5F1 (4) and were significantly overexpressed, including GLUT3 and REA NANOG (5). -
Detection of Interacting Transcription Factors in Human Tissues Using
Myšičková and Vingron BMC Genomics 2012, 13(Suppl 1):S2 http://www.biomedcentral.com/1471-2164/13/S1/S2 PROCEEDINGS Open Access Detection of interacting transcription factors in human tissues using predicted DNA binding affinity Alena Myšičková*, Martin Vingron From The Tenth Asia Pacific Bioinformatics Conference (APBC 2012) Melbourne, Australia. 17-19 January 2012 Abstract Background: Tissue-specific gene expression is generally regulated by combinatorial interactions among transcription factors (TFs) which bind to the DNA. Despite this known fact, previous discoveries of the mechanism that controls gene expression usually consider only a single TF. Results: We provide a prediction of interacting TFs in 22 human tissues based on their DNA-binding affinity in promoter regions. We analyze all possible pairs of 130 vertebrate TFs from the JASPAR database. First, all human promoter regions are scanned for single TF-DNA binding affinities with TRAP and for each TF a ranked list of all promoters ordered by the binding affinity is created. We then study the similarity of the ranked lists and detect candidates for TF-TF interaction by applying a partial independence test for multiway contingency tables. Our candidates are validated by both known protein-protein interactions (PPIs) and known gene regulation mechanisms in the selected tissue. We find that the known PPIs are significantly enriched in the groups of our predicted TF-TF interactions (2 and 7 times more common than expected by chance). In addition, the predicted interacting TFs for studied tissues (liver, muscle, hematopoietic stem cell) are supported in literature to be active regulators or to be expressed in the corresponding tissue. -
Table S1. List of Proteins in the BAHD1 Interactome
Table S1. List of proteins in the BAHD1 interactome BAHD1 nuclear partners found in this work yeast two-hybrid screen Name Description Function Reference (a) Chromatin adapters HP1α (CBX5) chromobox homolog 5 (HP1 alpha) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins (20-23) HP1β (CBX1) chromobox homolog 1 (HP1 beta) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins HP1γ (CBX3) chromobox homolog 3 (HP1 gamma) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins MBD1 methyl-CpG binding domain protein 1 Binds methylated CpG dinucleotide and chromatin-associated proteins (22, 24-26) Chromatin modification enzymes CHD1 chromodomain helicase DNA binding protein 1 ATP-dependent chromatin remodeling activity (27-28) HDAC5 histone deacetylase 5 Histone deacetylase activity (23,29,30) SETDB1 (ESET;KMT1E) SET domain, bifurcated 1 Histone-lysine N-methyltransferase activity (31-34) Transcription factors GTF3C2 general transcription factor IIIC, polypeptide 2, beta 110kDa Required for RNA polymerase III-mediated transcription HEYL (Hey3) hairy/enhancer-of-split related with YRPW motif-like DNA-binding transcription factor with basic helix-loop-helix domain (35) KLF10 (TIEG1) Kruppel-like factor 10 DNA-binding transcription factor with C2H2 zinc finger domain (36) NR2F1 (COUP-TFI) nuclear receptor subfamily 2, group F, member 1 DNA-binding transcription factor with C4 type zinc finger domain (ligand-regulated) (36) PEG3 paternally expressed 3 DNA-binding transcription factor with -
Disruption of the Neuronal PAS3 Gene in a Family Affected with Schizophrenia D Kamnasaran, W J Muir, M a Ferguson-Smith,Dwcox
325 ORIGINAL ARTICLE J Med Genet: first published as 10.1136/jmg.40.5.325 on 1 May 2003. Downloaded from Disruption of the neuronal PAS3 gene in a family affected with schizophrenia D Kamnasaran, W J Muir, M A Ferguson-Smith,DWCox ............................................................................................................................. J Med Genet 2003;40:325–332 Schizophrenia and its subtypes are part of a complex brain disorder with multiple postulated aetiolo- gies. There is evidence that this common disease is genetically heterogeneous, with many loci involved. See end of article for In this report, we describe a mother and daughter affected with schizophrenia, who are carriers of a authors’ affiliations t(9;14)(q34;q13) chromosome. By mapping on flow sorted aberrant chromosomes isolated from lym- ....................... phoblast cell lines, both subjects were found to have a translocation breakpoint junction between the Correspondence to: markers D14S730 and D14S70, a 683 kb interval on chromosome 14q13. This interval was found to Dr D W Cox, 8-39 Medical contain the neuronal PAS3 gene (NPAS3), by annotating the genomic sequence for ESTs and perform- Sciences Building, ing RACE and cDNA library screenings. The NPAS3 gene was characterised with respect to the University of Alberta, genomic structure, human expression profile, and protein cellular localisation to gain insight into gene Edmonton, Alberta T6G function. The translocation breakpoint junction lies within the third intron of NPAS3, resulting in the dis- 2H7, Canada; [email protected] ruption of the coding potential. The fact that the bHLH and PAS domains are disrupted from the remain- ing parts of the encoded protein suggests that the DNA binding and dimerisation functions of this Revised version received protein are destroyed. -
Transcription Factor P73 Regulates Th1 Differentiation
ARTICLE https://doi.org/10.1038/s41467-020-15172-5 OPEN Transcription factor p73 regulates Th1 differentiation Min Ren1, Majid Kazemian 1,4, Ming Zheng2, JianPing He3, Peng Li1, Jangsuk Oh1, Wei Liao1, Jessica Li1, ✉ Jonathan Rajaseelan1, Brian L. Kelsall 3, Gary Peltz 2 & Warren J. Leonard1 Inter-individual differences in T helper (Th) cell responses affect susceptibility to infectious, allergic and autoimmune diseases. To identify factors contributing to these response differ- 1234567890():,; ences, here we analyze in vitro differentiated Th1 cells from 16 inbred mouse strains. Haplotype-based computational genetic analysis indicates that the p53 family protein, p73, affects Th1 differentiation. In cells differentiated under Th1 conditions in vitro, p73 negatively regulates IFNγ production. p73 binds within, or upstream of, and modulates the expression of Th1 differentiation-related genes such as Ifng and Il12rb2. Furthermore, in mouse experimental autoimmune encephalitis, p73-deficient mice have increased IFNγ production and less dis- ease severity, whereas in an adoptive transfer model of inflammatory bowel disease, transfer of p73-deficient naïve CD4+ T cells increases Th1 responses and augments disease severity. Our results thus identify p73 as a negative regulator of the Th1 immune response, suggesting that p73 dysregulation may contribute to susceptibility to autoimmune disease. 1 Laboratory of Molecular Immunology and the Immunology Center, National Heart, Lung, and Blood Institute, Bethesda, MD 20892-1674, USA. 2 Department of Anesthesia, Stanford University School of Medicine, Stanford, CA 94305, USA. 3 Laboratory of Molecular Immunology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA. 4Present address: Department of Biochemistry and Computer Science, Purdue University, West ✉ Lafayette, IN 37906, USA. -
Multifactorial Erβ and NOTCH1 Control of Squamous Differentiation and Cancer
Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks, … , Karine Lefort, G. Paolo Dotto J Clin Invest. 2014;124(5):2260-2276. https://doi.org/10.1172/JCI72718. Research Article Oncology Downmodulation or loss-of-function mutations of the gene encoding NOTCH1 are associated with dysfunctional squamous cell differentiation and development of squamous cell carcinoma (SCC) in skin and internal organs. While NOTCH1 receptor activation has been well characterized, little is known about how NOTCH1 gene transcription is regulated. Using bioinformatics and functional screening approaches, we identified several regulators of the NOTCH1 gene in keratinocytes, with the transcription factors DLX5 and EGR3 and estrogen receptor β (ERβ) directly controlling its expression in differentiation. DLX5 and ERG3 are required for RNA polymerase II (PolII) recruitment to the NOTCH1 locus, while ERβ controls NOTCH1 transcription through RNA PolII pause release. Expression of several identified NOTCH1 regulators, including ERβ, is frequently compromised in skin, head and neck, and lung SCCs and SCC-derived cell lines. Furthermore, a keratinocyte ERβ–dependent program of gene expression is subverted in SCCs from various body sites, and there are consistent differences in mutation and gene-expression signatures of head and neck and lung SCCs in female versus male patients. Experimentally increased ERβ expression or treatment with ERβ agonists inhibited proliferation of SCC cells and promoted NOTCH1 expression and squamous differentiation both in vitro and in mouse xenotransplants. Our data identify a link between transcriptional control of NOTCH1 expression and the estrogen response in keratinocytes, with implications for differentiation therapy of squamous cancer. Find the latest version: https://jci.me/72718/pdf Research article Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks,1,2 Paola Ostano,3 Seung-Hee Jo,1,2 Jun Dai,1,2 Spiro Getsios,4 Piotr Dziunycz,5 Günther F.L. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
The Role of SAMHD1 in Restriction and Immune Sensing of Retroviruses and Retroelements
The role of SAMHD1 in restriction and immune sensing of retroviruses and retroelements Die Rolle von SAMHD1 in der Restriktion und Immunerkennung von Retroviren und Retroelementen Der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades Dr. rer. nat. vorgelegt von Alexandra Herrmann aus Biberach an der Riß Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg Tag der mündlichen Prüfung: 31.07.2018 Vorsitzender des Promotionsorgans: Prof. Dr. Georg Kreimer Gutachter: Prof. Dr. Lars Nitschke Prof. Dr. Manfred Marschall Table of content Table of content I. Summary ......................................................................................................................... 1 I. Zusammenfassung ......................................................................................................... 3 II. Introduction ..................................................................................................................... 5 1. The human immunodeficiency virus .................................................................................... 5 2. Transposable elements ......................................................................................................... 7 3. Host restriction factors ........................................................................................................ 10 4. The restriction factor SAMHD1 .......................................................................................... -
` Probing the Epigenome Andrea Huston1, Cheryl H Arrowsmith1,2
` Probing the Epigenome Andrea Huston1, Cheryl H Arrowsmith1,2, Stefan Knapp3,4,*, Matthieu Schapira1,5,* 1. Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada 2. Princess Margaret Cancer Centre and Department of Medical Biophysics, University of Toronto , Toronto, ON M5G 1L7, Canada 3. Nuffield Department of Clinical Medicine, Target Discovery Institute, and Structural Genomic Consortium, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom 4. Institute for Pharmaceutical Chemistry, Johann Wolfgang Goethe University, D-60438 Frankfurt am Main, Germany 5. Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5S 1A8, Canada * Correspondence: [email protected], [email protected] Epigenetic chemical probes are having a strong impact on biological discovery and target validation. Systematic coverage of emerging epigenetic target classes with these potent, selective, cell-active chemical tools will profoundly influence our understanding of the human biology and pathology of chromatin-templated mechanisms. ` Chemical probes are research-enablers Advances in genomics and proteomics methodologies in recent years have made it possible to associate thousands of genes and proteins with specific diseases, biological processes, molecular networks and pathways. However, data from these large scale initiatives alone has not translated widely into new studies on these disease-associated proteins, and the biomedical research community still tends to focus on proteins that were already known before the sequencing of the human genome1. The human kinome for instance, a target class of direct relevance to cancer and other disease areas, is a telling example: based on the number of research articles indexed in pubmed in 2011, 75% of the research activity focused on only 10% of the 518 human kinases – largely the same kinases that were the focus of research before sequencing of the human genome - while 60% of the kinome, some 300 enzymes, was virtually ignored by the community2. -
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 -
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.