DOCSLIB.ORG

Report of Genemania Search

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

GeneMANIA http://www.genemania.org/print Created on: 5 December 2012 00:06:51 Last database update: 19 July 2012 20:00:00 Application version: 3.1.1 Report of GeneMANIA search Network image POLR3H TAF1B POLR1B TAF1C POLR2L POLR2F RFWD2 0.33 Functions legend Networks legend transcription from RNA polymerase I promoter Co-expression query genes Co-localization Genetic interactions Pathway Physical interactions Predicted Shared protein domains 第1页 共25页 2012/12/5 13:27 GeneMANIA http://www.genemania.org/print Search parameters Organism: H. sapiens (human) Genes: TCOF1; TP53; POLR1C; POLR1D Networks: Attributes: Network weighting: Automatically selected weighting method (Biological process based) Number of gene results: 20 第2页 共25页 2012/12/5 13:27 GeneMANIA http://www.genemania.org/print Networks Co-expression 37.73 % Wang-Maris-2006 2.68 % Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Wang et al. (2006). Cancer Res . Source: Pearson correlation with 231,830 interactions from GEO Tags: transcription factors; cancer Ramaswamy-Golub-2001 2.44 % Multiclass cancer diagnosis using tumor gene expression signatures. Ramaswamy et al. (2001). Proc Natl Acad Sci U S A. Source: Pearson correlation with 226,615 interactions from supplementary material Tags: cancer Radtke-Downing-2009 2.33 % Genomic analysis reveals few genetic alterations in pediatric acute myeloid leukemia. Radtke et al. (2009). Proc Natl Acad Sci U S A . Source: Pearson correlation with 377,767 interactions from GEO Tags: transcription factors; nervous system; cancer Gobble-Singer-2011 2.15 % Expression profiling of liposarcoma yields a multigene predictor of patient outcome and identifies genes that contribute to liposarcomagenesis. Gobble et al. (2011). Cancer Res . Source: Pearson correlation with 449,507 interactions from GEO Tags: apoptosis; cell line; cultured cells; cancer; transcription factors Kang-Willman-2010 2.04 % Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia. Kang et al. (2010). Blood . Source: Pearson correlation with 549,783 interactions from GEO Tags: transcription factors; lymphoma; cancer Hummel-Siebert-2006 2.00 % A biologic definition of Burkitt's lymphoma from transcriptional and genomic profiling. Hummel et al. (2006). N Engl J Med . Source: Pearson correlation with 466,765 interactions from GEO Tags: cancer Jones-Libermann-2005 1.99 % Gene signatures of progression and metastasis in renal cell cancer. Jones et al. (2005). Clin Cancer Res . Source: Pearson correlation with 365,428 interactions from GEO Tags: transcription factors; disease; cancer Bild-Nevins-2006 B 1.96 % Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Bild et al. (2006). Nature . Note: One of 3 datasets produced from this publication. Source: Pearson correlation with 254,105 interactions from GEO Tags: cultured cells; signal transduction; cancer; epithelial cells; cell line; disease; breast; transcription factors; breast cancer Noble-Diehl-2008 1.92 % Regional variation in gene expression in the healthy colon is dysregulated in ulcerative colitis. Noble et al. (2008). Gut . Source: Pearson correlation with 603,016 interactions from GEO Wu-Garvey-2007 1.89 % The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Wu et al. (2007). 第3页 共25页 2012/12/5 13:27 GeneMANIA http://www.genemania.org/print Endocrine . Source: Pearson correlation with 223,988 interactions from GEO Tags: transcription factors; muscle; cultured cells Nakayama-Hasegawa-2007 1.89 % Gene expression analysis of soft tissue sarcomas: characterization and reclassification of malignant fibrous histiocytoma. Nakayama et al. (2007). Mod Pathol . Source: Pearson correlation with 361,398 interactions from GEO Tags: transcription factors; cancer Burington-Shaughnessy-2008 1.88 % Tumor cell gene expression changes following short-term in vivo exposure to single agent chemotherapeutics are related to survival in multiple myeloma. Burington et al. (2008). Clin Cancer Res . Source: Pearson correlation with 266,464 interactions from GEO Tags: transcription factors; time series; cancer; chemotherapy Burczynski-Dorner-2006 1.77 % Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. Burczynski et al. (2006). J Mol Diagn . Source: Pearson correlation with 419,356 interactions from GEO Raue-Trappe-2012 1.72 % Transcriptome signature of resistance exercise adaptations: mixed muscle and fiber type specific profiles in young and old adults. Raue et al. (2012). J Appl Physiol . Source: Pearson correlation with 483,849 interactions from GEO Rieger-Chu-2004 1.72 % Toxicity from radiation therapy associated with abnormal transcriptional responses to DNA damage. Rieger et al. (2004). Proc Natl Acad Sci U S A . Source: Pearson correlation with 220,846 interactions from GEO Tags: cultured cells; cell line Arijs-Rutgeerts-2009 1.58 % Mucosal gene expression of antimicrobial peptides in inflammatory bowel disease before and after first infliximab treatment. Arijs et al. (2009). PLoS One . Source: Pearson correlation with 570,210 interactions from GEO Tags: immune system Peng-Katze-2009 1.54 % Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers. Peng et al. (2009). BMC Genomics . Source: Pearson correlation with 349,954 interactions from GEO Tags: liver Berchtold-Cotman-2008 1.53 % Gene expression changes in the course of normal brain aging are sexually dimorphic. Berchtold et al. (2008). Proc Natl Acad Sci U S A . Source: Pearson correlation with 507,933 interactions from GEO Tags: aging; brain Toedter-Baribaud-2011 1.49 % Gene expression profiling and response signatures associated with differential responses to infliximab treatment in ulcerative colitis. Toedter et al. (2011). Am J Gastroenterol . Source: Pearson correlation with 572,082 interactions from GEO Tags: signal transduction; immune system Perou-Botstein-1999 1.18 % Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Perou et al. (1999). Proc Natl Acad Sci U S A . 第4页 共25页 2012/12/5 13:27 GeneMANIA http://www.genemania.org/print Source: Pearson correlation with 50,804 interactions from supplementary material Tags: cultured cells; cancer; epithelial cells; signal transduction; breast; stromal cells; transcription factors; breast cancer Physical interactions 32.73 % Behzadnia-Lührmann-2007 8.91 % Composition and three-dimensional EM structure of double affinity-purified, human prespliceosomal A complexes. Behzadnia et al. (2007). EMBO J . Source: Direct interaction with 101 interactions from iRefIndex Kneissl-Grummt-2003 5.94 % Interaction and assembly of murine pre-replicative complex proteins in yeast and mouse cells. Kneissl et al. (2003). J Mol Biol . Source: Direct interaction with 83 interactions from iRefIndex Tags: cell proliferation; transcription factors; cultured cells; cell line McFarland-Nussbaum-2008 4.19 % Proteomics analysis identifies phosphorylation-dependent alpha-synuclein protein interactions. McFarland et al. (2008). Mol Cell Proteomics . Source: Direct interaction with 152 interactions from iRefIndex Tags: brain; nervous system Hutchins-Peters-2010 3.22 % Systematic analysis of human protein complexes identifies chromosome segregation proteins. Hutchins et al. (2010). Science . Source: Direct interaction with 141 interactions from iRefIndex Tags: cell proliferation; nervous system; localization Jeronimo-Coulombe-2007 2.10 % Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme. Jeronimo et al. (2007). Mol Cell . Source: Direct interaction with 660 interactions from iRefIndex Tags: cultured cells; cell line Ramachandran-LaBaer-2004 1.26 % Self-assembling protein microarrays. Ramachandran et al. (2004). Science . Source: Direct interaction with 119 interactions from iRefIndex Goudreault-Gingras-2009 1.13 % A PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) protein. Goudreault et al. (2009). Mol Cell Proteomics . Source: Direct interaction with 242 interactions from iRefIndex Tags: nervous system Sato-Conaway-2004 1.05 % A set of consensus mammalian mediator subunits identified by multidimensional protein identification technology. Sato et al. (2004). Mol Cell . Source: Direct interaction with 235 interactions from iRefIndex Tags: transcription factors Barr-Knapp-2009 0.75 % Large-scale structural analysis of the classical human protein tyrosine phosphatome. Barr et al. (2009). Cell . Source: Direct interaction with 177 interactions from iRefIndex IREF-MPPI 0.52 % Source: Direct interaction with 385 interactions from iRefIndex Jones-MacBeath-2006 0.47 % A quantitative protein interaction network for the ErbB receptors using protein microarrays. Jones et al. (2006). Nature . 第5页 共25页 2012/12/5 13:27 GeneMANIA http://www.genemania.org/print Source: Direct interaction with 139 interactions from iRefIndex Tags: cultured cells; cell line Sowa-Harper-2009 A 0.42 % Defining the human deubiquitinating enzyme interaction landscape. Sowa et al. (2009).
Recommended publications
  • Supplementary Materials

    Supplementary Materials

    DEPs in osteosarcoma cells comparing to osteoblastic cells Biological Process Protein Percentage of Hits metabolic process (GO:0008152) 29.3 29.3% cellular process (GO:0009987) 20.2 20.2% localization (GO:0051179) 9.4 9.4% biological regulation (GO:0065007) 8 8.0% developmental process (GO:0032502) 7.8 7.8% response to stimulus (GO:0050896) 5.6 5.6% cellular component organization (GO:0071840) 5.6 5.6% multicellular organismal process (GO:0032501) 4.4 4.4% immune system process (GO:0002376) 4.2 4.2% biological adhesion (GO:0022610) 2.7 2.7% apoptotic process (GO:0006915) 1.6 1.6% reproduction (GO:0000003) 0.8 0.8% locomotion (GO:0040011) 0.4 0.4% cell killing (GO:0001906) 0.1 0.1% 100.1% Genes 2179Hits 3870 biological adhesion apoptotic process … reproduction (GO:0000003) , 0.8% (GO:0022610) , 2.7% locomotion (GO:0040011) ,… immune system process cell killing (GO:0001906) , 0.1% (GO:0002376) , 4.2% multicellular organismal process (GO:0032501) , metabolic process 4.4% (GO:0008152) , 29.3% cellular component organization (GO:0071840) , 5.6% response to stimulus (GO:0050896), 5.6% developmental process (GO:0032502) , 7.8% biological regulation (GO:0065007) , 8.0% cellular process (GO:0009987) , 20.2% localization (GO:0051179) , 9.
  • POLR2L Antibody Cat

    POLR2L Antibody Cat

    POLR2L Antibody Cat. No.: XW-7445 POLR2L Antibody Specifications HOST SPECIES: Chicken SPECIES REACTIVITY: Human, Mouse, Rat IMMUNOGEN: 1-67 TESTED APPLICATIONS: WB POLR2L antibody can be used for the detection of POLR2L by Western blot, may also work APPLICATIONS: for IHC and ICC. PREDICTED MOLECULAR 7.6 kDa (calculated) WEIGHT: Properties PURIFICATION: Antigen affinity-purified CLONALITY: Polyclonal CONJUGATE: Unconjugated PHYSICAL STATE: Liquid BUFFER: Phosphate-Buffered Saline. No preservatives added. CONCENTRATION: 1 mg/mL October 1, 2021 1 https://www.prosci-inc.com/polr2l-antibody-7445.html POLR2L antibody can be stored at 4˚C for short term (weeks). Long term storage should STORAGE CONDITIONS: be at -20˚C. As with all antibodies care should be taken to avoid repeated freeze thaw cycles. Antibodies should not be exposed to prolonged high temperatures. Additional Info OFFICIAL SYMBOL: POLR2L DNA-directed RNA polymerases I, II, and III subunit RPABC5, DNA-directed RNA ALTERNATE NAMES: polymerase III subunit L, RNA polymerases I, and III subunit ABC5, RBP10, RPB10, RPABC5, RPB7.6, hRPB7.6, RPB10beta, POLR2L ACCESSION NO.: NP_066951.1 PROTEIN GI NO.: 10863925 GENE ID: 5441 USER NOTE: Optimal dilutions for each application to be determined by the researcher. Background and References DNA directed RNA polymerase II polypeptide L; polymerase (RNA) II (DNA directed) polypeptide L (7.6kD); RNA polymerase II subunit. This protein is a subunit of RNA polymerase II, the polymerase responsible for synthesizing messenger RNA in eukaryotes. BACKGROUND: It contains four conserved cysteines characteristic of an atypical zinc-binding domain. Like its counterpart in yeast, this subunit may be shared by the other two DNA-directed RNA polymerases.
  • A Network Propagation Approach to Prioritize Long Tail Genes in Cancer

    A Network Propagation Approach to Prioritize Long Tail Genes in Cancer

    bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429983; this version posted February 8, 2021. 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. A Network Propagation Approach to Prioritize Long Tail Genes in Cancer Hussein Mohsen1,*, Vignesh Gunasekharan2, Tao Qing2, Sahand Negahban3, Zoltan Szallasi4, Lajos Pusztai2,*, Mark B. Gerstein1,5,6,3,* 1 Computational Biology & Bioinformatics Program, Yale University, New Haven, CT 06511, USA 2 Breast Medical Oncology, Yale School of Medicine, New Haven, CT 06511, USA 3 Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA 4 Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA 5 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA 6 Department of Computer Science, Yale University, New Haven, CT 06511, USA * Corresponding author Abstract Introduction. The diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • AVILA-DISSERTATION.Pdf

    AVILA-DISSERTATION.Pdf

    LOGOS EX MACHINA: A REASONED APPROACH TOWARD CANCER by Andrew Avila, B. S., M. S. A Dissertation In Biological Sciences Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Approved Lauren Gollahon Chairperson of the Committee Rich Strauss Sean Rice Boyd Butler Richard Watson Peggy Gordon Miller Dean of the Graduate School May, 2012 c 2012, Andrew Avila Texas Tech University, Andrew Avila, May 2012 ACKNOWLEDGEMENTS I wish to acknowledge the incredible support given to me by my major adviser, Dr. Lauren Gollahon. Without your guidance surely I would not have made it as far as I have. Furthermore, the intellectual exchange I have shared with my advisory committee these long years have propelled me to new heights of inquiry I had not dreamed of even in the most lucid of my imaginings. That their continual intellectual challenges have provoked and evoked a subtle sense of natural wisdom is an ode to their efficacy in guiding the aspirant to the well of knowledge. For this initiation into the mysteries of nature I cannot thank my advisory committee enough. I also wish to thank the Vice President of Research for the fellowship which sustained the initial couple years of my residency at Texas Tech. Furthermore, my appreciation of the support provided to me by the Biology Department, financial and otherwise, cannot be understated. Finally, I also wish to acknowledge the individuals working at the High Performance Computing Center, without your tireless support in maintaining the cluster I would have not have completed the sheer amount of research that I have.
  • Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79

    Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79

    www.nature.com/scientificreports OPEN Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment Jifeng Liu1,2, Tengfei Ma1,2, Mingzhong Gao3, Yilin Liu4, Jun Liu1, Shichao Wang2, Yike Xie2, Ling Wang2, Juan Cheng2, Shixi Liu1*, Jian Zou1,2*, Jiang Wu2, Weimin Li2 & Heping Xie2,3,5 As resources in the shallow depths of the earth exhausted, people will spend extended periods of time in the deep underground space. However, little is known about the deep underground environment afecting the health of organisms. Hence, we established both deep underground laboratory (DUGL) and above ground laboratory (AGL) to investigate the efect of environmental factors on organisms. Six environmental parameters were monitored in the DUGL and AGL. Growth curves were recorded and tandem mass tag (TMT) proteomics analysis were performed to explore the proliferative ability and diferentially abundant proteins (DAPs) in V79 cells (a cell line widely used in biological study in DUGLs) cultured in the DUGL and AGL. Parallel Reaction Monitoring was conducted to verify the TMT results. γ ray dose rate showed the most detectable diference between the two laboratories, whereby γ ray dose rate was signifcantly lower in the DUGL compared to the AGL. V79 cell proliferation was slower in the DUGL. Quantitative proteomics detected 980 DAPs (absolute fold change ≥ 1.2, p < 0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. KEGG pathway analysis revealed that seven pathways (e.g.
  • H4K16 Acetylation Marks Active Genes and Enhancers of Embryonic Stem Cells, but Does Not Alter Chromatin Compaction

    H4K16 Acetylation Marks Active Genes and Enhancers of Embryonic Stem Cells, but Does Not Alter Chromatin Compaction

    Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press H4K16 acetylation marks active genes and enhancers of embryonic stem cells, but does not alter chromatin compaction Gillian Taylor1, Ragnhild Eskeland2, Betül Hekimoglu-Balkan1, Madapura M. Pradeepa1* and Wendy A Bickmore1* 1 MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine at University of Edinburgh, Crewe Road, Edinburgh EH4 2XU, UK 2Current address: Department of Molecular Biosciences, University of Oslo, N-0316 Oslo, Norway *Correspondence to: W. Bickmore or M.M. Pradeepa, MRC Human Genetics Unit, MRC IGMM, Crewe Road, Edinburgh EH4 2XU, UK Tel: +44 131 332 2471 Fax: +44 131 467 8456 Email:[email protected] or [email protected] Running head: H4K16 acetylation and long-range genome regulation Keywords: Chromatin compaction, embryonic stem cells, fluorescence in situ hybridization, histone acetylation, long-range regulation, 1 Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract Compared with histone H3, acetylation of H4 tails has not been well studied, especially in mammalian cells. Yet, H4K16 acetylation is of particular interest because of its ability to decompact nucleosomes in vitro and its involvement in dosage compensation in flies. Here we show that, surprisingly, loss of H4K16 acetylation does not alter higher-order chromatin compaction in vivo in mouse embryonic stem cells (ESCs). As well as peaks of acetylated H4K16 and Kat8/MOF histone acetyltransferase at the transcription start sites of expressed genes, we report that acetylation of H4K16 is a new marker of active enhancers in ESCs and that some enhancers are marked by H3K4me1, Kat8 and H4K16ac but not by acetylated H3K27 or p300/EP300, suggesting that they are novel EP300 independent regulatory elements.
  • Psychosis: a Convergent Functional Genomics Approach Identifying a Series of Candidate Genes for Mania

    Psychosis: a Convergent Functional Genomics Approach Identifying a Series of Candidate Genes for Mania

    Identifying a series of candidate genes for mania and psychosis: a convergent functional genomics approach ALEXANDER B. NICULESCU, III, DAVID S. SEGAL, RONALD KUCZENSKI, THOMAS BARRETT, RICHARD L. HAUGER and JOHN R. KELSOE Physiol. Genomics 4:83-91, 2000. You might find this additional information useful... This article cites 52 articles, 15 of which you can access free at: http://physiolgenomics.physiology.org/cgi/content/full/4/1/83#BIBL This article has been cited by 4 other HighWire hosted articles: Evaluation of common gene expression patterns in the rat nervous system S. Kaiser and L. K. Nisenbaum Physiol Genomics, December 16, 2003; 16 (1): 1-7. [Abstract] [Full Text] [PDF] Microarray Technology: A Review of New Strategies to Discover Candidate Vulnerability Downloaded from Genes in Psychiatric Disorders W. E. Bunney, B. G. Bunney, M. P. Vawter, H. Tomita, J. Li, S. J. Evans, P. V. Choudary, R. M. Myers, E. G. Jones, S. J. Watson and H. Akil Am. J. Psychiatry, April 1, 2003; 160 (4): 657-666. [Abstract] [Full Text] [PDF] Biomarker Identification by Feature Wrappers physiolgenomics.physiology.org M. Xiong, X. Fang and J. Zhao Genome Res., November 1, 2001; 11 (11): 1878-1887. [Abstract] [Full Text] [PDF] GRK3 mediates desensitization of CRF1 receptors: a potential mechanism regulating stress adaptation F. M. Dautzenberg, S. Braun and R. L. Hauger Am J Physiol Regulatory Integrative Comp Physiol, April 1, 2001; 280 (4): R935-946. [Abstract] [Full Text] Medline items on this article's topics can be found at http://highwire.stanford.edu/lists/artbytopic.dtl on the following topics: on August 11, 2005 Immunology .
  • Bidirectional Cooperation Between Ubtf1 and SL1 Determines RNA Polymerase I Promoter

    Bidirectional Cooperation Between Ubtf1 and SL1 Determines RNA Polymerase I Promoter

    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.07.447350; this version posted June 7, 2021. 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 4.0 International license. 1 Bidirectional cooperation between Ubtf1 and SL1 determines RNA Polymerase I promoter 2 recognition in cell and is negatively affected in the UBTF-E210K neuroregression syndrome. 3 4 Michel G. Tremblay1, Dany S. Sibai1,2, Melissa Valère1,2, Jean-Clément Mars1,2,+, Frédéric Lessard1, 5 Roderick T. Hori3, Mohammad M. Khan4, Victor Y. Stefanovsky1, Mark S. Ledoux5 and Tom Moss1,2*. 6 7 1Laboratory of Growth and Development, St-Patrick Research Group in Basic Oncology, Cancer 8 Division of the Quebec University Hospital Research Centre, Québec, Canada. 2Department of 9 Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, 10 Québec, Canada. 3Departments of Microbiology, Immunology and Biochemistry and 4Departments of 11 Neurology and Anatomy & Neurobiology, University of Tennessee Health Science Center, Memphis, 12 TN, USA. 5Department of Psychology, University of Memphis, Memphis TN and Veracity 13 Neuroscience LLC, Memphis, TN 14 15 +Present address, IRIC, Université de Montréal, Montréal, Québec, Canada 16 17 Correspondence should be addressed to; 18 Tom Moss, PhD, 19 Edifice St Patrick, 9 rue McMahon, Québec, QC, G1R 3S3, Canada. 20 E-mail. [email protected] 21 Tel. 1 418 691 5281 22 FAX 1 418 691 5439 23 24 Short title: Ubtf1-SL1 cooperation and the Ubtf-E210K syndrome.
  • Essential Genes and Their Role in Autism Spectrum Disorder

    Essential Genes and Their Role in Autism Spectrum Disorder

    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Essential Genes And Their Role In Autism Spectrum Disorder Xiao Ji University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, and the Genetics Commons Recommended Citation Ji, Xiao, "Essential Genes And Their Role In Autism Spectrum Disorder" (2017). Publicly Accessible Penn Dissertations. 2369. https://repository.upenn.edu/edissertations/2369 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2369 For more information, please contact [email protected]. Essential Genes And Their Role In Autism Spectrum Disorder Abstract Essential genes (EGs) play central roles in fundamental cellular processes and are required for the survival of an organism. EGs are enriched for human disease genes and are under strong purifying selection. This intolerance to deleterious mutations, commonly observed haploinsufficiency and the importance of EGs in pre- and postnatal development suggests a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication and repetitive behavior. More and more genetic evidence points to a polygenic model of ASD and it is estimated that hundreds of genes contribute to ASD. The central question addressed in this dissertation is whether genes with a strong effect on survival and fitness (i.e. EGs) play a specific oler in ASD risk. I compiled a comprehensive catalog of 3,915 mammalian EGs by combining human orthologs of lethal genes in knockout mice and genes responsible for cell-based essentiality.
  • POLR1D Gene RNA Polymerase I and III Subunit D

    POLR1D Gene RNA Polymerase I and III Subunit D

    POLR1D gene RNA polymerase I and III subunit D Normal Function The POLR1D gene provides instructions for making one part (subunit) of two related enzymes called RNA polymerase I and RNA polymerase III. These enzymes are involved in the production (synthesis) of ribonucleic acid (RNA), a chemical cousin of DNA. Both enzymes help synthesize a form of RNA known as ribosomal RNA (rRNA). RNA polymerase III also plays a role in the synthesis of several other forms of RNA, including transfer RNA (tRNA). Ribosomal RNA and transfer RNA assemble protein building blocks (amino acids) into functioning proteins, which is essential for the normal functioning and survival of cells. Based on its involvement in Treacher Collins syndrome, the POLR1D gene appears to play a critical role in the early development of structures that become bones and other tissues of the face. Health Conditions Related to Genetic Changes Treacher Collins syndrome At least 20 mutations in the POLR1D gene have been identified in people with Treacher Collins syndrome, a condition that affects the development of bones and other tissues of the face. These mutations appear to alter the structure and function of the POLR1D protein, which reduces the amount of functional RNA polymerase I and RNA polymerase III in cells. Consequently, less rRNA is produced. Researchers speculate that a shortage of rRNA may trigger the self-destruction (apoptosis) of certain cells involved in the early development of facial bones and tissues. The abnormal cell death could underlie the specific problems with facial development found in Treacher Collins syndrome. However, it is unclear why the effects of a reduction in rRNA are limited to facial development.
  • Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?

    Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?

    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.