The Interactions of Differentially Expressed Genes

Total Page:16

File Type:pdf, Size:1020Kb

The Interactions of Differentially Expressed Genes THE INTERACTIONS OF DIFFERENTIALLY EXPRESSED GENES DIRECTLY TARGETED BY WNT\β-CATENIN SIGNALING: IMPLICATIONS FOR COLORECTAL CARCINOGENESIS By Charles F. Murchison A thesis presented to the Department of Medical Informatics and Clinical Epidemiology and the Oregon Health & Science University School of Medicine in partial fulfillment of the requirements for the degree of Masters of Science December 2010 School of Medicine Oregon Health & Science University Certificate of Approval This is to certify that the Master’s Thesis of Charles F. Murchison “The Interactions of Differentially Expressed Genes Directly Targeted by Wnt\β-catenin Signaling: Implications for Colorectal Carcinogenesis” Has been approved _________________________________________ Thesis Advisor _________________________________________ Committee Member _________________________________________ Committee Member TABLE OF CONTENTS LIST OF TABLES ..................................................................................................................... v LIST OF FIGURES .................................................................................................................. vi ACKNOWLEDGEMENTS ...................................................................................................... vii ABSTRACT .......................................................................................................................... viii CHAPTER 1 – INTRODUCTION 1.1 COLON CANCER .................................................................................................... 1 1.2 ADENOMA DEVELOPMENT AND THE WNT SIGNALING PATHWAY...................... 4 1.3 MICROARRAYS AND POOLED ANALYSES .............................................................. 6 1.4 OTHER HIGH-THROUGHPUT TECHNIQUES ........................................................... 9 1.5 A MULTI-METHOD APPROACH ........................................................................... 12 CHAPTER 2 – METHODS 2.1 LITERATURE SEARCH FOR EXPRESSION STUDIES .............................................. 14 2.2 NORMALIZATION OF CEL FILES TO ABSOLUTE INTENSITY ................................ 15 2.3 DIFFERENTIAL EXPRESSION ............................................................................... 18 2.4 CO-EXPRESSION................................................................................................. 19 i 2.5 MODULE COMPARISON: PATIENT VS CELL-LINE ................................................ 19 2.6 WNT TRANSCRIPTION FACTOR BINDING DATA .................................................. 20 2.7 EXPRESSION PATTERNS AND β-CATENIN BINDING ............................................ 21 2.8 THE PROTEIN-PROTEIN INTERACTION NETWORKS ............................................ 22 2.9 CODINGS FOR THE NETWORK ............................................................................ 24 2.10 EVALUATION OF THE DISCOVERY NETWORK ................................................... 26 2.11 NULL NETWORK MODEL – COMPARING OBSERVED AND PERMUTED PROPORTIONS OF METRICS ............................................................................. 28 CHAPTER 3 – RESULTS 3.1 LITERATURE SEARCH OF mRNA EXPRESSION STUDIES ...................................... 32 3.2 NORMALIZATION ASSESSMENT FOR POOLING .................................................. 33 3.3 AGGREGATE STUDY OF MRNA EXPRESSION ...................................................... 33 3.4 FILTERING OF SACO DATA OF β-CATENIN BINDING ........................................... 36 3.5 THE PROTEIN PRODUCT INTERACTION NETWORK............................................. 36 3.6 NETWORK NODES WITH SINGLE METRICS OF INTEREST ................................... 37 3.7 NODES MATCHING MULTIPLE CRITERIA AND A FINAL NETWORK ..................... 38 3.8 ASSESSMENT OF THE NULL MODEL ................................................................... 40 3.9 ANNOTATION OF FUNCTION FOR IDENTIFIED GENES/NODES .......................... 41 ii CHAPTER 4 – DISCUSSION 4.1 INITIAL ANALYSIS – DIFFERENTIAL EXPRESSION PROFILES AND DIRECT β-CATENIN TARGETING ...................................................................................... 44 4.2 BIOLOGICAL ANNOTATION – THE 8 FUNCTIONAL ACTIVITY CATEGORIES FOR THE REFINED NETWORK ............................................................................. 47 4.3 CELL GROWTH, PROLIFERATION AND REGULATION OF THE CELL CYCLE .......... 48 4.4 PHOSPHORYLATION AND UBIQUITINATION ...................................................... 49 4.5 CELL FATE AND DIFFERENTIATION, AND EPITHELIAL CELL POLARITY ................ 50 4.6 EPIGENETICS AND HISTONE MODIFICATION ...................................................... 51 4.7 CELL-CELL INTERACTIONS, MOLECULAR TRANSPORT, INTER-CELLULAR SIGNALING .......................................................................................................... 52 4.8 PARTICIPANTS IN THE CANONICAL WNT PATHWAY – COFACTORS AND RECEPTORS ................................................................................................. 54 4.9 UBIQUITOUSLY EXPRESSED GENES – GENERALIZED FUNCTIONALITY AND MULTIPLE BIOLOGICAL CIRCUMSTANCES .................................................. 56 4.10 EXPRESSION LOCALIZED OUTSIDE THE COLON ................................................ 57 4.11 OBSERVATIONS ON NODES IN THE CANONICAL WNT PATHWAY.................... 59 4.12 FINAL TRENDS ................................................................................................... 60 CHAPTER 5 – CONCLUSIONS 5.1 LIMITATIONS OF THE CURRENT STUDY .............................................................. 64 5.2 FUTURE DIRECTIONS .......................................................................................... 65 iii 5.3 CONTRIBUTIONS TO THE FIELD .......................................................................... 66 REFERENCE LIST ................................................................................................................ 69 TABLES ............................................................................................................................... 83 FIGURES ........................................................................................................................... 111 iv LIST OF TABLES TABLE 1 – STUDIES USED FOR mRNA ANALYSIS ............................................................... 83 TABLE 2 – SIGNIFICANCE COUNTS OF PROBES FROM mRNA ANALYSIS .......................... 84 TABLE 3 – CONCORDANT GENES BY DIRECTION OF DIFFERENTIAL EXPRESSION ............ 85 TABLE 4 – TOP 5 CONCORDANTLY UPREGULATED AND DOWNREGULATED GENES ....... 86 TABLE 5 – DIRECT TARGETS OF β-CATENIN SHOWING UPREGULATION AND DOWNREGULATION ................................................................................. 88 TABLE 6 – METRICS OF THE INITIAL PROTEIN INTERACTION NETWORK .......................... 95 TABLE 7 – GENES WITH MRNA OVEREXPRESSION FOUND IN ADENOMAS AND CELL LINES ................................................................................................ 96 TABLE 8 – GENES WITH MRNA UNDEREXPRESSION FOUND IN ADENOMAS AND CELL LINES ................................................................................................ 97 TABLE 9 – METRICS OF NODES WITH ALTERED CO-EXPRESSION UNDER ADENOMATOUS CONDITIONS ........................................................................ 98 TABLE 10 – METRICS FOR β-CATENIN DIRECT TARGETS AND NEIGHBORS ...................... 99 TABLE 11 – METRICS FOR INTERACTION NETWORK HUBS AND HUB NEIGHBORS ........ 101 TABLE 12 – METRICS FOR KNOWN WNT PARTICIPANTS AND NEIGHBORS ................... 105 TABLE 13 – FULL METRICS LISTING FOR THE FINAL 65 NETWORK NODES ..................... 107 TABLE 14 – STATISTICAL ASSESSMENT OF NULL NETWORK MODEL .............................. 110 v LIST OF FIGURES FIGURE 1 – COLONIC CRYPT AND ADENOCARCINOMA DEVELOPMENT ........................ 111 FIGURE 2 – WNT ACTIVATION OF β-CATENIN DEPENDENT TRANSCRIPTION ................ 113 FIGURE 3 – SERIAL ANALYSIS OF CHROMATIN OCCUPANCY .......................................... 115 FIGURE 4 – DENSITY GRAPH OF SPOT INTENSITIES FROM mRNA AGGREGATION ANALYSIS ............................................................................. 117 FIGURE 5 – BOX PLOTS OF INDIVIDUAL ARRAYS COMPARING NORMALIZATIONS ........ 119 FIGURE 6 – VENN DIAGRAM FOR DIFFERENTIALLY EXPRESSED GENES BY MODULE GROUPING ............................................................................... 123 FIGURE 7 – OVERVIEW OF THE BASE PROTEIN INTERACTION NETWORK ...................... 125 FIGURE 8 – COLOR CODED LEGEND FOR TABLES 7-12 ................................................... 127 FIGURE 9 – REFINED INTERACTION NETWORK USED FOR VISUALIZATION ................... 129 vi ACKNOWLEDGEMENTS This thesis would not have been possible without the guidance and support of my advisor Dr. Shannon McWeeney. Her tireless devotion and formidable intellect are matched only by her enthusiasm for the study of Bioinformatics and the entire discipline benefits from her involvement. Words do little justice to the generosity and insight she has given over the years but thank you, Shannon, for everything. Equally central in the development and implementation of this project were the members of my thesis advisory committee, Drs. Greg Yochum and Motomi Mori. Such a broad and inter-disciplinary study required a great deal of understanding of numerous disparate fields. Their advice in molecular biology and microarray analysis were indispensible in bringing this
Recommended publications
  • HTLV-1 Tax Stimulates Molecular Events in Various Cancers
    Fortune J Health Sci 2021; 4 (1): 160-190 DOI: 10.26502/fjhs016 Review Article HTLV-1 Tax Stimulates Molecular Events in Various Cancers Wanyi Zhu1, Igor F Tsigelny2, 3, Valentina L Kouznetsova2* 1MAP Program, San Diego Supercomputer Center, UC San Diego, San Diego, CA, USA 2San Diego Supercomputer Center, UC San Diego, La Jolla, CA, 92093, USA 3Department of Neurosciences, UC San Diego, San Diego, CA, USA *Corresponding Author: Valentina L Kouznetsova, San Diego Supercomputer Center, UC San Diego, La Jolla, CA, 92093, USA; E-mail: [email protected] Received: 16 December 2020; Accepted: 26 December 2020; Published: 04 January 2021 Citation: Wanyi Zhu, Igor F Tsigelny, Valentina L Kouznetsova. HTLV-1 Tax Stimulates Molecular Events in Various Cancers. Fortune Journal of Health Sciences 4 (2021): 160-190. Abstract contributes epigenetically to the development of The human T-lymphotropic viruses (HTLV) are a different cancers by altering normal transcription and family of retroviruses that causes adult T-cell translation, cell cycle signaling systems, and tumor leukemia/lymphoma (ATL). The objective of this suppressing mechanisms. The discovered effects of study is to elucidate the host genes affected by the Tax on the NF-κB, MAPK, Cyclin-CDK, ErbB, Jak- HTLV-1 Tax protein, find how host genes are STAT, VEGF, TGF-β, PI3K-Akt, and β-catenin affected, and how this influence is related to several pathways active during the progression of pancreatic cancer pathways. The DAVID program was used to cancer, chronic myeloid leukemia, small cell lung examine genes affected by Tax, and the gene list was cancer, and colorectal cancer indicates that HTLV- significantly enriched: pancreatic cancer, chronic and 1’s effects are not limited to ATL, can activate acute myeloid leukemia, small cell lung cancer, cancer-specific biomarkers including ZEB-1, ZEB-2, prostate cancer, non-small cell lung cancer, colorectal EZH2, E2F, TRIM33, GLUT1, HK2, PKM2, and cancer, glioma, melanoma, and bladder cancer.
    [Show full text]
  • Supp Material.Pdf
    Simon et al. Supplementary information: Table of contents p.1 Supplementary material and methods p.2-4 • PoIy(I)-poly(C) Treatment • Flow Cytometry and Immunohistochemistry • Western Blotting • Quantitative RT-PCR • Fluorescence In Situ Hybridization • RNA-Seq • Exome capture • Sequencing Supplementary Figures and Tables Suppl. items Description pages Figure 1 Inactivation of Ezh2 affects normal thymocyte development 5 Figure 2 Ezh2 mouse leukemias express cell surface T cell receptor 6 Figure 3 Expression of EZH2 and Hox genes in T-ALL 7 Figure 4 Additional mutation et deletion of chromatin modifiers in T-ALL 8 Figure 5 PRC2 expression and activity in human lymphoproliferative disease 9 Figure 6 PRC2 regulatory network (String analysis) 10 Table 1 Primers and probes for detection of PRC2 genes 11 Table 2 Patient and T-ALL characteristics 12 Table 3 Statistics of RNA and DNA sequencing 13 Table 4 Mutations found in human T-ALLs (see Fig. 3D and Suppl. Fig. 4) 14 Table 5 SNP populations in analyzed human T-ALL samples 15 Table 6 List of altered genes in T-ALL for DAVID analysis 20 Table 7 List of David functional clusters 31 Table 8 List of acquired SNP tested in normal non leukemic DNA 32 1 Simon et al. Supplementary Material and Methods PoIy(I)-poly(C) Treatment. pIpC (GE Healthcare Lifesciences) was dissolved in endotoxin-free D-PBS (Gibco) at a concentration of 2 mg/ml. Mice received four consecutive injections of 150 μg pIpC every other day. The day of the last pIpC injection was designated as day 0 of experiment.
    [Show full text]
  • Associated 16P11.2 Deletion in Drosophila Melanogaster
    ARTICLE DOI: 10.1038/s41467-018-04882-6 OPEN Pervasive genetic interactions modulate neurodevelopmental defects of the autism- associated 16p11.2 deletion in Drosophila melanogaster Janani Iyer1, Mayanglambam Dhruba Singh1, Matthew Jensen1,2, Payal Patel 1, Lucilla Pizzo1, Emily Huber1, Haley Koerselman3, Alexis T. Weiner 1, Paola Lepanto4, Komal Vadodaria1, Alexis Kubina1, Qingyu Wang 1,2, Abigail Talbert1, Sneha Yennawar1, Jose Badano 4, J. Robert Manak3,5, Melissa M. Rolls1, Arjun Krishnan6,7 & 1234567890():,; Santhosh Girirajan 1,2,8 As opposed to syndromic CNVs caused by single genes, extensive phenotypic heterogeneity in variably-expressive CNVs complicates disease gene discovery and functional evaluation. Here, we propose a complex interaction model for pathogenicity of the autism-associated 16p11.2 deletion, where CNV genes interact with each other in conserved pathways to modulate expression of the phenotype. Using multiple quantitative methods in Drosophila RNAi lines, we identify a range of neurodevelopmental phenotypes for knockdown of indi- vidual 16p11.2 homologs in different tissues. We test 565 pairwise knockdowns in the developing eye, and identify 24 interactions between pairs of 16p11.2 homologs and 46 interactions between 16p11.2 homologs and neurodevelopmental genes that suppress or enhance cell proliferation phenotypes compared to one-hit knockdowns. These interac- tions within cell proliferation pathways are also enriched in a human brain-specific network, providing translational relevance in humans. Our study indicates a role for pervasive genetic interactions within CNVs towards cellular and developmental phenotypes. 1 Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA. 2 Bioinformatics and Genomics Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
    [Show full text]
  • The Viral Oncoproteins Tax and HBZ Reprogram the Cellular Mrna Splicing Landscape
    bioRxiv preprint doi: https://doi.org/10.1101/2021.01.18.427104; this version posted January 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape Charlotte Vandermeulen1,2,3, Tina O’Grady3, Bartimee Galvan3, Majid Cherkaoui1, Alice Desbuleux1,2,4,5, Georges Coppin1,2,4,5, Julien Olivet1,2,4,5, Lamya Ben Ameur6, Keisuke Kataoka7, Seishi Ogawa7, Marc Thiry8, Franck Mortreux6, Michael A. Calderwood2,4,5, David E. Hill2,4,5, Johan Van Weyenbergh9, Benoit Charloteaux2,4,5,10, Marc Vidal2,4*, Franck Dequiedt3*, and Jean-Claude Twizere1,2,11* 1Laboratory of Viral Interactomes, GIGA Institute, University of Liege, Liege, Belgium.2Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.3Laboratory of Gene Expression and Cancer, GIGA Institute, University of Liege, Liege, Belgium.4Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. 5Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.6Laboratory of Biology and Modeling of the Cell, CNRS UMR 5239, INSERM U1210, University of Lyon, Lyon, France.7Department of Pathology and Tumor Biology, Kyoto University, Japan.8Unit of Cell and Tissue Biology, GIGA Institute, University of Liege, Liege, Belgium.9Laboratory of Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Catholic University of Leuven, Leuven, Belgium.10Department of Human Genetics, CHU of Liege, University of Liege, Liege, Belgium.11Lead Contact. *Correspondence: [email protected]; [email protected]; [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2021.01.18.427104; this version posted January 18, 2021.
    [Show full text]
  • Bioinformatics Analysis to Screen Key Genes in Papillary Thyroid Carcinoma
    ONCOLOGY LETTERS 19: 195-204, 2020 Bioinformatics analysis to screen key genes in papillary thyroid carcinoma YUANHU LIU1*, SHUWEI GAO2*, YAQIONG JIN2, YERAN YANG2, JUN TAI1, SHENGCAI WANG1, HUI YANG2, PING CHU2, SHUJING HAN2, JIE LU2, XIN NI1,2, YONGBO YU2 and YONGLI GUO2 1Department of Otolaryngology, Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health; 2Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, MOE Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, P.R. China Received April 22, 2019; Accepted September 24, 2019 DOI: 10.3892/ol.2019.11100 Abstract. Papillary thyroid carcinoma (PTC) is the most verifying their potential for clinical diagnosis. Taken together, common type of thyroid carcinoma, and its incidence has the findings of the present study suggest that these genes and been on the increase in recent years. However, the molecular related pathways are involved in key events of PTC progression mechanism of PTC is unclear and misdiagnosis remains a and facilitate the identification of prognostic biomarkers. major issue. Therefore, the present study aimed to investigate this mechanism, and to identify key prognostic biomarkers. Introduction Integrated analysis was used to explore differentially expressed genes (DEGs) between PTC and healthy thyroid tissue. To Thyroid carcinoma is the most common malignancy of investigate the functions and pathways associated with DEGs, the head and neck, and accounts for 91.5% of all endocrine Gene Ontology, pathway and protein-protein interaction (PPI) malignancies (1).
    [Show full text]
  • Cell Leukemia Virus Tax Oncoprotein
    bioRxiv preprint doi: https://doi.org/10.1101/2021.08.25.457680; this version posted August 25, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Interactome and structural basis for targeting the human T- cell leukemia virus Tax oncoprotein Sibusiso B. Maseko1, Inge Van Molle2, Karim Blibek1, Christoph Gorgulla3-5, Julien Olivet1, Jeremy Blavier1, Charlotte Vandermeulen1, Stéphanie Skupiewski1, Deeya Saha1, Thandokuhle Ntombela6, Julianne Lim7, Frederique Lembo8, Aurelie Beauvois9, Malik Hamaidia9, Jean-Paul Borg8, Pascale Zimmermann8,10, Frank Delvigne11, Luc Willems9,11, Johan Van Weyenbergh12, Dae-Kyum Kim7, 13-15, Franck Dequiedt16, Haribabu Arthanari3-5, Alexander N. Volkov2,17,*, Jean-Claude Twizere1,11,18,* 1Laboratory of Viral Interactomes, Unit of Molecular Biology of Diseases, GIGA Institute, University of Liege, Liège, Belgium. 2VIB-VUB Center for Structural Biology, Flemish Institute of Biotechnology (VIB), Pleinlaan 2, Brussels, Belgium. 3Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. 4Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA. 5Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. 6Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu Natal, Durban 4001, South Africa. 7Center for Personalized Medicine, Roswell Park Comprehensive Cancer Cen- ter, Buffalo, New York, USA. 8Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Equipe labellisée Ligue ‘Cell polarity, Cell signaling and Cancer, Marseille, France. 9Laboratory of Cellular and Molecular Epigenetics, Cancer Unit, GIGA Institute, University of Liege, Liege, Belgium. 10Department of Human Genetics, KU Leuven, Belgium.
    [Show full text]
  • T-, B-And NK-Lymphoid, but Not Myeloid Cells Arise from Human
    Leukemia (2007) 21, 311–319 & 2007 Nature Publishing Group All rights reserved 0887-6924/07 $30.00 www.nature.com/leu ORIGINAL ARTICLE T-, B- and NK-lymphoid, but not myeloid cells arise from human CD34 þ CD38ÀCD7 þ common lymphoid progenitors expressing lymphoid-specific genes I Hoebeke1,3, M De Smedt1, F Stolz1,4, K Pike-Overzet2, FJT Staal2, J Plum1 and G Leclercq1 1Department of Clinical Chemistry, Microbiology and Immunology, Ghent University Hospital, Ghent, Belgium and 2Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands Hematopoietic stem cells in the bone marrow (BM) give rise to share a direct common progenitor either, as CLPs were not all blood cells. According to the classic model of hematopoi- found in the fetal liver.5 Instead, fetal B and T cells would esis, the differentiation paths leading to the myeloid and develop through B/myeloid and T/myeloid intermediates. lymphoid lineages segregate early. A candidate ‘common 6 lymphoid progenitor’ (CLP) has been isolated from The first report of a human CLP came from Galy et al. who À þ CD34 þ CD38À human cord blood cells based on CD7 expres- showed that a subpopulation of adult and fetal BM Lin CD34 sion. Here, we confirm the B- and NK-differentiation potential of cells expressing the early B- and T-cell marker CD10 is not þ À þ CD34 CD38 CD7 cells and show in addition that this capable of generating monocytic, granulocytic, erythroid or population has strong capacity to differentiate into T cells. As megakaryocytic cells, but can differentiate into dendritic cells, CD34 þ CD38ÀCD7 þ cells are virtually devoid of myeloid B, T and NK cells.
    [Show full text]
  • Milger Et Al. Pulmonary CCR2+CD4+ T Cells Are Immune Regulatory And
    Milger et al. Pulmonary CCR2+CD4+ T cells are immune regulatory and attenuate lung fibrosis development Supplemental Table S1 List of significantly regulated mRNAs between CCR2+ and CCR2- CD4+ Tcells on Affymetrix Mouse Gene ST 1.0 array. Genewise testing for differential expression by limma t-test and Benjamini-Hochberg multiple testing correction (FDR < 10%). Ratio, significant FDR<10% Probeset Gene symbol or ID Gene Title Entrez rawp BH (1680) 10590631 Ccr2 chemokine (C-C motif) receptor 2 12772 3.27E-09 1.33E-05 9.72 10547590 Klrg1 killer cell lectin-like receptor subfamily G, member 1 50928 1.17E-07 1.23E-04 6.57 10450154 H2-Aa histocompatibility 2, class II antigen A, alpha 14960 2.83E-07 1.71E-04 6.31 10590628 Ccr3 chemokine (C-C motif) receptor 3 12771 1.46E-07 1.30E-04 5.93 10519983 Fgl2 fibrinogen-like protein 2 14190 9.18E-08 1.09E-04 5.49 10349603 Il10 interleukin 10 16153 7.67E-06 1.29E-03 5.28 10590635 Ccr5 chemokine (C-C motif) receptor 5 /// chemokine (C-C motif) receptor 2 12774 5.64E-08 7.64E-05 5.02 10598013 Ccr5 chemokine (C-C motif) receptor 5 /// chemokine (C-C motif) receptor 2 12774 5.64E-08 7.64E-05 5.02 10475517 AA467197 expressed sequence AA467197 /// microRNA 147 433470 7.32E-04 2.68E-02 4.96 10503098 Lyn Yamaguchi sarcoma viral (v-yes-1) oncogene homolog 17096 3.98E-08 6.65E-05 4.89 10345791 Il1rl1 interleukin 1 receptor-like 1 17082 6.25E-08 8.08E-05 4.78 10580077 Rln3 relaxin 3 212108 7.77E-04 2.81E-02 4.77 10523156 Cxcl2 chemokine (C-X-C motif) ligand 2 20310 6.00E-04 2.35E-02 4.55 10456005 Cd74 CD74 antigen
    [Show full text]
  • Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks
    Providence St. Joseph Health Providence St. Joseph Health Digital Commons Articles, Abstracts, and Reports 1-1-2017 Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks. Hongdong Li Yang Zhang Yuanfang Guan Rajasree Menon Gilbert S Omenn Follow this and additional works at: https://digitalcommons.psjhealth.org/publications HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Methods Manuscript Author Mol Biol. Author Manuscript Author manuscript; available in PMC 2018 May 24. Published in final edited form as: Methods Mol Biol. 2017 ; 1558: 415–436. doi:10.1007/978-1-4939-6783-4_20. Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks Hongdong Li, Yang Zhang, Yuanfang Guan, Rajasree Menon, and Gilbert S. Omenn Abstract Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies. Keywords Functional prediction; Isoform network; Protein folding; Splice isoforms 1 Introduction One of the most remarkable developments in biological evolution is the emergence in multicellular organisms of gene structures with exons and introns.
    [Show full text]
  • Identification and Interaction Analysis of Molecular Markers in Pancreatic
    medRxiv preprint doi: https://doi.org/10.1101/2020.12.20.20248601; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Integrated Bioinformatics Analysis and Molecular Docking Experiments Basavaraj Vastrad1 , Chanabasayya Vastrad *2, Anandkumar Tengli3 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics,Chanabasava Nilaya, Bharthinagar, Dharwad, Karanataka 580001, India. 3. Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Mysuru and JSS Academy of Higher Education & Research, Mysuru- 570015, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.20.20248601; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract The current investigation aimed to mine therapeutic molecular targets that play an key part in the advancement of pancreatic ductal adenocarcinoma (PDAC). The expression profiling by high throughput sequencing dataset profile GSE133684 dataset was downloaded from the Gene Expression Omnibus (GEO) database.
    [Show full text]
  • The 57 Kb Deletion in Cystinosis Patients Extends Into TRPV1
    Downloaded from jmg.bmj.com on May 9, 2011 - Published by group.bmj.com JMG Online First, published on May 5, 2011 as 10.1136/jmg.2010.083303 Short report The 57 kb deletion in cystinosis patients extends into TRPV1 causing dysregulation of transcription in peripheral blood mononuclear cells Katy A Freed,1 John Blangero,1 Tom Howard,2 Matthew P Johnson,1 Joanne E Curran,1 Yvonne R Garcia,1 Hao-Chang Lan,1 Hanna E Abboud,3 Eric K Moses1 1Department of Genetics, Texas ABSTRACT 17 in 1995.4 In 1998, positional cloning was used to Biomedical Research Institute, Background Cystinosis is an autosomal recessive isolate the CTNS gene (HGNC: 2518).5 CTNS San Antonio, Texas, USA 2 disease characterised by the abnormal accumulation of encodes the lysosomal cystine transport protein, Department of Pathology and CTNS Laboratory Medicine, Veterans lysosomal cystine. Mutations in the cystinosin gene cystinosin, and mutations in the gene Affairs Greater Los Angeles (CTNS) represent known causes for the disease. The represent the known causes of cystinosis. CTNS is Healthcare System, Los major cystinosis mutation is a 57 kb deletion on human composed of 12 exons5 and at least 90 CTNS e Angeles, California, USA chromosome 17p13 that removes the majority of CTNS mutations have been described,6 8 the most 3Department of Medicine, University of Texas Health and the entire adjacent gene, CARKL/SHPK. common being a 57 257 bp deletion (57 kb deletion) 9 Science Center, San Antonio, Objectives In order to identify other genes that may located on human chromosome 17p13. The Texas, USA influence the cystinosis pathobiological pathway, deletion breakpoints for the 57 kb deletion were peripheral blood mononuclear cells (PBMC) were characterised9 and were found to extend from Correspondence to collected from cystinosis family members, and DNA and within exon 10 of CTNS and upstream through the Katy A Freed, Department of CARKL/SHPK Genetics, Texas Biomedical RNA extracted.
    [Show full text]
  • TAX1BP3 (NM 014604) Human Untagged Clone – SC319925
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for SC319925 TAX1BP3 (NM_014604) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: TAX1BP3 (NM_014604) Human Untagged Clone Tag: Tag Free Symbol: TAX1BP3 Synonyms: TIP-1; TIP1 Vector: pCMV6-AC (PS100020) E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: Neomycin Fully Sequenced ORF: >OriGene sequence for NM_014604.2 GGGGACTCTGCTGCCGGCTTCTCGGAGCGGCGCTGGGCGACCAGAGCAGGGTCGAGATGT CCTACATCCCGGGCCAGCCGGTCACCGCCGTGGTGCAAAGAGTTGAAATTCACAAGCTGC GTCAAGGTGAGAACTTAATCCTGGGTTTCAGCATTGGAGGTGGAATCGACCAGGACCCTT CCCAGAATCCCTTCTCTGAAGACAAGACGGACAAGGGTATTTATGTCACACGGGTGTCTG AAGGAGGCCCTGCTGAAATCGCTGGGCTGCAGATTGGAGACAAGATCATGCAGGTGAACG GCTGGGACATGACCATGGTCACACACGACCAGGCCCGCAAGCGGCTCACCAAGCGCTCGG AGGAGGTGGTGCGTCTGCTGGTGACGCGGCAGTCGCTGCAGAAGGCCGTGCAGCAGTCCA TGCTGTCCTAGCAGCCACCACCATCTGCGACTCCTGCCTGCCGCCTCTCTGTACAGTAAC GCCACTTCCACACTCTGTCCCCATCTGGCTTCTGCTGACCGCTGGGCCCCAGCTCAGAAG GGCTATAGCTGGTCCCAGAGGCCTGGCCTGGCCTTCCTTCCCTTCTCCCATCCCTGGCCT GGGGCCTCTGGGACCAGCTTTCTCTCCTGGACACCGAGGATTGGAAATAAGGGCCTGGAG CTGAGTAGTAGCCAGTCTGCTGTGACCACAGGCTCAGGTCCGACCCTGCTGCTTGGCCAC AGCAGTGGCTGGGCAAGTGGGAACCACTATCTCTTGGGAGCCCCCAAAAGCTGGGAAATG CTGGAGGAACCAGGCCTTTCCCGCTTTTGCCTGGCTGCAGGGTTCGGCTCCGCCCCTGCC CCCCAGCCCTCGTGTGTCCACATCGCAGTGCCTCTGCCCCTCGGGGGACTGGACACACAT CCTGCCAGAGGCGCTACGAAGCTTTGCCCAGATGAAGCCAGGTGGGCTCCGCGTTCACTC CCACTCTCCCGAGGGGTGCTGGCCTCCCCAGGGTTTGCCTTCTTACGGATTTAGACGAGG
    [Show full text]