G9a Selectively Represses a Class of Late-Replicating Genes at the Nuclear Periphery
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Expression of Cancer-Testis Antigens MAGEA1, MAGEA3, ACRBP, PRAME, SSX2, and CTAG2 in Myxoid and Round Cell Liposarcoma
Modern Pathology (2014) 27, 1238–1245 1238 & 2014 USCAP, Inc All rights reserved 0893-3952/14 $32.00 Expression of cancer-testis antigens MAGEA1, MAGEA3, ACRBP, PRAME, SSX2, and CTAG2 in myxoid and round cell liposarcoma Jessica A Hemminger1, Amanda Ewart Toland2, Thomas J Scharschmidt3, Joel L Mayerson3, Denis C Guttridge2 and O Hans Iwenofu1 1Department of Pathology and Laboratory Medicine, Wexner Medical Center at The Ohio State University, Columbus, OH, USA; 2Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University Wexner Medical Center, Columbus, OH, USA and 3Department of Orthopedics, The Ohio State University Wexner Medical Center, Columbus, OH, USA Myxoid and round-cell liposarcoma is a frequently encountered liposarcoma subtype. The mainstay of treatment remains surgical excision with or without chemoradiation. However, treatment options are limited in the setting of metastatic disease. Cancer-testis antigens are immunogenic antigens with the expression largely restricted to testicular germ cells and various malignancies, making them attractive targets for cancer immunotherapy. Gene expression studies have reported the expression of various cancer-testis antigens in liposarcoma, with mRNA expression of CTAG1B, CTAG2, MAGEA9, and PRAME described specifically in myxoid and round-cell liposarcoma. Herein, we further explore the expression of the cancer-testis antigens MAGEA1, ACRBP, PRAME, and SSX2 in myxoid and round-cell liposarcoma by immunohistochemistry in addition to determining mRNA levels of CTAG2 (LAGE-1), PRAME, and MAGEA3 by quantitative real-time PCR. Samples in formalin-fixed paraffin-embedded blocks (n ¼ 37) and frozen tissue (n ¼ 8) were obtained for immunohistochemistry and quantitative real-time PCR, respectively. Full sections were stained with antibodies to MAGEA1, ACRBP, PRAME, and SSX2 and staining was assessed for intensity (1–2 þ ) and percent tumor positivity. -
Functional Analysis of Structural Variation in the 2D and 3D Human Genome
FUNCTIONAL ANALYSIS OF STRUCTURAL VARIATION IN THE 2D AND 3D HUMAN GENOME by Conor Mitchell Liam Nodzak A dissertation submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics and Computational Biology Charlotte 2019 Approved by: Dr. Xinghua Mindy Shi Dr. Rebekah Rogers Dr. Jun-tao Guo Dr. Adam Reitzel ii c 2019 Conor Mitchell Liam Nodzak ALL RIGHTS RESERVED iii ABSTRACT CONOR MITCHELL LIAM NODZAK. Functional analysis of structural variation in the 2D and 3D human genome. (Under the direction of DR. XINGHUA MINDY SHI) The human genome consists of over 3 billion nucleotides that have an average distance of 3.4 Angstroms between each base, which equates to over two meters of DNA contained within the 125 µm3 volume diploid cell nuclei. The dense compaction of chromatin by the supercoiling of DNA forms distinct architectural modules called topologically associated domains (TADs), which keep protein-coding genes, noncoding RNAs and epigenetic regulatory elements in close nuclear space. It has recently been shown that these conserved chromatin structures may contribute to tissue-specific gene expression through the encapsulation of genes and cis-regulatory elements, and mutations that affect TADs can lead to developmental disorders and some forms of cancer. At the population-level, genomic structural variation contributes more to cumulative genetic difference than any other class of mutation, yet much remains to be studied as to how structural variation affects TADs. Here, we study the func- tional effects of structural variants (SVs) through the analysis of chromatin topology and gene activity for three trio families sampled from genetically diverse popula- tions from the Human Genome Structural Variation Consortium. -
Functional Annotation of Genes Overlapping Copy Number Variants in Autistic Patients: Focus on Axon Pathfinding
136 Current Genomics, 2010, 11, 136-145 Functional Annotation of Genes Overlapping Copy Number Variants in Autistic Patients: Focus on Axon Pathfinding Silvia Sbacchi1, Francesco Acquadro2, Ignazio Calò1, Francesco Calì3 and Valentino Romano*,1,3 1Dipartimento di Oncologia Sperimentale e Applicazioni Cliniche, Università degli Studi di Palermo, Palermo; 2Molecular Cytogenetics Group, Centro Nacional de Investigaciones Oncologicas (C.N.I.O.), and Centro de Investiga- ciones de Enfermidades Raras (CIBERER), Madrid, Spain; 3Associazione Oasi Maria SS. (I.R.C.C.S.), Troina (EN), Italy Abstract: We have used Gene Ontology (GO) and pathway analyses to uncover the common functions associated to the genes overlapping Copy Number Variants (CNVs) in autistic patients. Our source of data were four published studies [1- 4]. We first applied a two-step enrichment strategy for autism-specific genes. We fished out from the four mentioned stud- ies a list of 2928 genes overall overlapping 328 CNVs in patients and we first selected a sub-group of 2044 genes after excluding those ones that are also involved in CNVs reported in the Database of Genomic Variants (enrichment step 1). We then selected from the step 1-enriched list a sub-group of 514 genes each of which was found to be deleted or dupli- cated in at least two patients (enrichment step 2). The number of statistically significant processes and pathways identified by the Database for Annotation, Visualization and Integrated Discovery and Ingenuity Pathways Analysis softwares with the step 2-enriched list was significantly higher compared to the step 1-enriched list. In addition, statistically significant GO terms, biofunctions and pathways related to nervous system development and function were exclusively identified by the step 2-enriched list of genes. -
The Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease
University of Groningen Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease van der Harst, Pim; Verweij, Niek Published in: Circulation research DOI: 10.1161/CIRCRESAHA.117.312086 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2018 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): van der Harst, P., & Verweij, N. (2018). Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease. Circulation research, 122(3), 433-443. https://doi.org/10.1161/CIRCRESAHA.117.312086 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne- amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. -
Genome-Wide Analysis of Cancer/Testis Gene Expression
Genome-wide analysis of cancer/testis gene expression Oliver Hofmanna,b,1, Otavia L. Caballeroc, Brian J. Stevensond,e, Yao-Tseng Chenf, Tzeela Cohenc, Ramon Chuac, Christopher A. Maherb, Sumir Panjib, Ulf Schaeferb, Adele Krugerb, Minna Lehvaslaihob, Piero Carnincig,h, Yoshihide Hayashizakig,h, C. Victor Jongeneeld,e, Andrew J. G. Simpsonc, Lloyd J. Oldc,1, and Winston Hidea,b aDepartment of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, SPH2, 4th Floor, Boston, MA 02115; bSouth African National Bioinformatics Institute, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa; cLudwig Institute for Cancer Research, New York Branch at Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021; dLudwig Institute for Cancer Research, Lausanne Branch, 1015 Lausanne, Switzerland; eSwiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; fWeill Medical College of Cornell University, 1300 York Avenue, New York, NY 10021; gGenome Exploration Research Group (Genome Network Project Core Group), RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan; and hGenome Science Laboratory, Discovery Research Institute, RIKEN Wako Institute, 2-1 Hirosawa, Wako, Saitama, 3510198, Japan Contributed by Lloyd J. Old, October 28, 2008 (sent for review June 6, 2008) Cancer/Testis (CT) genes, normally expressed in germ line cells but expression profile information frequently limited to the original also activated in a wide range of cancer types, often encode defining articles. In some cases, e.g., ACRBP, the original antigens that are immunogenic in cancer patients, and present CT-restricted expression in normal tissues could not be con- potential for use as biomarkers and targets for immunotherapy. -
A Gene Expression Signature Identifying Transient DNMT1
Cannuyer et al. Clinical Epigenetics (2015) 7:114 DOI 10.1186/s13148-015-0147-4 RESEARCH Open Access A gene expression signature identifying transient DNMT1 depletion as a causal factor of cancer-germline gene activation in melanoma Julie Cannuyer, Aurélie Van Tongelen, Axelle Loriot and Charles De Smet* Abstract Background: Many human tumors show aberrant activation of a group of germline-specific genes, termed cancer- germline (CG) genes, several of which appear to exert oncogenic functions. Although activation of CG genes in tumors has been linked to promoter DNA demethylation, the mechanisms underlying this epigenetic alteration remain unclear. Twomainprocesseshavebeenproposed:awakingofagametogenic program directing demethylation of target DNA sequences via specific regulators, or general deficiency of DNA methylation activities resulting from mis-targeting or down-regulation of the DNMT1 methyltransferase. Results: By the analysis of transcriptomic data, we searched to identify gene expression changes associated with CG gene activation in melanoma cells. We found no evidence linking CG gene activation with differential expression of gametogenic regulators. Instead, CG gene activation correlated with decreased expression of a set of mitosis/division- related genes (ICCG genes). Interestingly, a similar gene expression signature was previously associated with depletion of DNMT1. Consistently, analysis of a large set of melanoma tissues revealed that DNMT1 expression levels were often lower in samples showing activation of multiple CG genes. Moreover, by using immortalized melanocytes and fibroblasts carrying an inducible anti-DNMT1 small hairpin RNA (shRNA), we demonstrate that transient depletion of DNMT1 can lead to long-term activation of CG genes and repression of ICCG genes at the same time. -
In-Silico Discovery of Cancer-Specific Peptide-HLA Complexes for Targeted Therapy Ankur Dhanik*, Jessica R
Dhanik et al. BMC Bioinformatics (2016) 17:286 DOI 10.1186/s12859-016-1150-2 RESEARCH ARTICLE Open Access In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy Ankur Dhanik*, Jessica R. Kirshner, Douglas MacDonald, Gavin Thurston, Hsin C. Lin, Andrew J. Murphy and Wen Zhang Abstract Background: Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested in peptide-HLA complexes involving peptides that are derived from proteins specifically expressed in cancer cells. Such complexes have been shown to provide an effective means of precisely targeting cancer cells by engineered T-cells and antibodies, which would be an improvement over current chemotherapeutic agents that indiscriminately kill proliferating cells. An important concern with the targeting of peptide-HLA complexes is off-target toxicity that could occur due to the presence of complexes similar to the target complex in cells from essential, normal tissues. Results: We developed a novel computational strategy for identifying potential peptide-HLA cancer targets and evaluating the likelihood of off-target toxicity associated with these targets. Our strategy combines sequence-based and structure-based approaches in a unique way to predict potential off-targets. The focus of our work is on the complexes involving the most frequent HLA class I allele HLA-A*02:01. Using our strategy, we predicted the off-target toxicity observed in past clinical trials. We employed it to perform a first-ever comprehensive exploration of the human peptidome to identify cancer-specific targets utilizing gene expression data from TCGA (The Cancer Genome Atlas) and GTEx (Gene Tissue Expression), and structural data from PDB (Protein Data Bank). -
Mouse Asz1 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Asz1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Asz1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Asz1 gene (NCBI Reference Sequence: NM_023729 ; Ensembl: ENSMUSG00000010796 ) is located on Mouse chromosome 6. 13 exons are identified, with the ATG start codon in exon 1 and the TAA stop codon in exon 13 (Transcript: ENSMUST00000010940). Exon 3~4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Asz1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-39F14 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Homozygous null male mice are sterile resulting from a block in spermatid development. Exon 3 starts from about 14.46% of the coding region. The knockout of Exon 3~4 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 3504 bp, and the size of intron 4 for 3'-loxP site insertion: 26235 bp. The size of effective cKO region: ~1899 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 3 4 13 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Asz1 Homology arm cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein–Protein Interaction Networks
International Journal of Molecular Sciences Article Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein–Protein Interaction Networks Suyu Mei 1,* and Kun Zhang 2,* 1 Software College, Shenyang Normal University, Shenyang 110034, China 2 Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA * Correspondence: [email protected] (S.M.); [email protected] (K.Z.) Received: 27 September 2019; Accepted: 11 October 2019; Published: 12 October 2019 Abstract: Rapid reconstruction of genome-scale protein–protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as “Neglog” this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L2-regularized logistic regression is used as the base classifier to counteract noise and train on a large dataset. Computational results show that the proposed Neglog method outperforms pure random sampling method with sound biological interpretability. -
Specification and Epigenetic Resetting of the Pig Germline Exhibit Conservation with the Human Lineage
bioRxiv preprint doi: https://doi.org/10.1101/2020.08.07.241075; this version posted August 7, 2020. 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-ND 4.0 International license. Specification and epigenetic resetting of the pig germline exhibit conservation with the human lineage Qifan Zhu1#, Fei Sang2#, Sarah Withey1^§, Walfred Tang3,4^, Sabine Dietmann3^, Doris Klisch1, Priscila Ramos-Ibeas1§, Haixin Zhang1§, Cristina E. Requena5,6, Petra Hajkova5,6, Matt Loose2, M. Azim Surani3,4,7*, Ramiro Alberio1* 1 School of Biosciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK. 2 School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK. 3 Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK. 4 Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK. 5 MRC London Institute of Medical Sciences (LMS), London, UK. 6 Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK. 7 Wellcome Trust Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK § Current address: P.R-I.: Animal Reproduction Department, National Institute for Agricultural and Food Research and Technology, Madrid 28040, Spain; S.W.: Stem Cell Engineering Group, Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Building 75, St Lucia, QLD 4072, Australia. # Equal contribution, ^ Equal contribution * Co-corresponding authors Email addresses of corresponding authors: [email protected]; [email protected] Lead contact: Ramiro Alberio 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.08.07.241075; this version posted August 7, 2020. -
A Novel Protein-DNA Interaction Involved with the Cpg Dinucleotide At
ARTICLECell Research, 14 (4), Aug 2004 Cell Research (2004); Jie 14 ZHANG (4):283-294 et al http://www.cell-research.com A novel protein-DNA interaction involved with the CpG dinucleotide at -30 upstream is linked to the DNA methylation mediated transcription silencing of the MAGE-A1 gene Jie ZHANG, Jian YU, Jun GU, Bao Mei GAO, Ying Jun ZHAO, Peng WANG, Hong Yu ZHANG, Jing De ZHU* The State-key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University, LN 2200/25, Xietu Road, Shanghai 200032, China. ABSTRACT To understand the DNA-methylation mediated gene silencing mechanisms, we analyzed in cell culture of the pro- moter function of the MAGE-A1 gene, which is frequently demethylated and over-expressed in human hepatocellular carcinoma. We have established the correlation of the DNA methylation of the promoter CpG island with expression status of this gene in a panel of the established liver cancer cell lines. The crucial CpG dinucleotide(s) within the minimal promoter subjected to the control mediated by DNA methylation with profound biological functions was also delineated. Furthermore, a novel sequence-specific DNA-protein interaction at the -30 CpG dinucleotide upstream of the gene was found having a vital part to play in the DNA methylation mediated transcription silencing of the MAGE-A1 gene. Our results would not only provide new insights into the DNA methylation mediated mechanisms over transcription of the MAGE-A1 gene, but also pave the way for further defining the cross-talk among DNA methylation, histone modifica- tion and chromatin remodeling in detail. -
(MAGEA1) (NM 004988) Human Tagged ORF Clone Product Data
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 RC202134 MAGE 1 (MAGEA1) (NM_004988) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: MAGE 1 (MAGEA1) (NM_004988) Human Tagged ORF Clone Tag: Myc-DDK Symbol: MAGEA1 Synonyms: CT1.1; MAGE1 Vector: pCMV6-Entry (PS100001) E. coli Selection: Kanamycin (25 ug/mL) Cell Selection: Neomycin ORF Nucleotide >RC202134 ORF sequence Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGTCTCTTGAGCAGAGGAGTCTGCACTGCAAGCCTGAGGAAGCCCTTGAGGCCCAACAAGAGGCCCTGG GCCTGGTGTGTGTGCAGGCTGCCGCCTCCTCCTCCTCTCCTCTGGTCCTGGGCACCCTGGAGGAGGTGCC CACTGCTGGGTCAACAGATCCTCCCCAGAGTCCTCAGGGAGCCTCCGCCTTTCCCACTACCATCAACTTC ACTCGACAGAGGCAACCCAGTGAGGGTTCCAGCAGCCGTGAAGAGGAGGGGCCAAGCACCTCTTGTATCC TGGAGTCCTTGTTCCGAGCAGTAATCACTAAGAAGGTGGCTGATTTGGTTGGTTTTCTGCTCCTCAAATA TCGAGCCAGGGAGCCAGTCACAAAGGCAGAAATGCTGGAGAGTGTCATCAAAAATTACAAGCACTGTTTT CCTGAGATCTTCGGCAAAGCCTCTGAGTCCTTGCAGCTGGTCTTTGGCATTGACGTGAAGGAAGCAGACC CCACCGGCCACTCCTATGTCCTTGTCACCTGCCTAGGTCTCTCCTATGATGGCCTGCTGGGTGATAATCA GATCATGCCCAAGACAGGCTTCCTGATAATTGTCCTGGTCATGATTGCAATGGAGGGCGGCCATGCTCCT GAGGAGGAAATCTGGGAGGAGCTGAGTGTGATGGAGGTGTATGATGGGAGGGAGCACAGTGCCTATGGGG AGCCCAGGAAGCTGCTCACCCAAGATTTGGTGCAGGAAAAGTACCTGGAGTACCGGCAGGTGCCGGACAG TGATCCCGCACGCTATGAGTTCCTGTGGGGTCCAAGGGCCCTTGCTGAAACCAGCTATGTGAAAGTCCTT GAGTATGTGATCAAGGTCAGTGCAAGAGTTCGCTTTTTCTTCCCATCCCTGCGTGAAGCAGCTTTGAGAG