The Integrator Complex Terminates Promoter-Proximal Transcription at Protein- Coding Genes
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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. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
PDF Output of CLIC (Clustering by Inferred Co-Expression)
PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 14 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 14. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Cpsf3l Polr2b Ints3 Ints7 Ints1 Ints4 Ints9 Ints2 -
PHF22 (INTS12) (NM 020395) Human Over-Expression Lysate 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 LC412498 PHF22 (INTS12) (NM_020395) Human Over-expression Lysate Product data: Product Type: Over-expression Lysates Description: INTS12 HEK293T cell transient overexpression lysate (as WB positive control) Species: Human Expression Host: HEK293T Expression cDNA Clone TrueORF Clone RC204838 or AA Sequence: Tag: C-Myc/DDK Detection Antibodies: Clone OTI4C5, Anti-DDK (FLAG) monoclonal antibody (TA50011-100) Accession Number: NM_020395, NP_065128 Synonyms: INT12; PHF22; SBBI22 Predicted MW: 48.8 kDa Components: 1 vial of 20 ug lyophilized gene specific transient over-expression cell lysate Storage: The lysate can be shipped at ambient temperature. Upon receiving, store the sample at - 20°C. Lysate samples can be reconstituted with SDS Sample Buffer. Avoid repeated freeze- thaw cycles after reconstitution. Lysate samples are stable for 12 months from date of receipt when stored at -20°C. Preparation: HEK293T cells in 10-cm dishes were transiently transfected withM egaTran Transfection Reagent (TT200002) and 5ug TrueORF cDNA plasmid. Transfected cells were cultured for 48hrs before collection. The cells were lysed in modified RIPA buffer (25mM Tris-HCl pH7.6, 150mM NaCl, 1% NP-40, 1mM EDTA, 1xProteinase inhibitor cocktail mix (Sigma), 1mM PMSF and 1mM Na3VO4), and then centrifuged to clarify the lysate. Protein concentration was measured by BCA kit (Thermo Scientific Inc.). To facilitate transportation and protein, the products are supplied as lyophilized proteins. RefSeq: NP_065128 Locus ID: 57117 Cytogenetics: 4q24 Protein Families: Druggable Genome, Transcription Factors This product is to be used for laboratory only. -
Bioinformatics Analysis for the Identification of Differentially Expressed Genes and Related Signaling Pathways in H
Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells Dingyu Chen*, Chao Li, Yan Zhao, Jianjiang Zhou, Qinrong Wang and Yuan Xie* Key Laboratory of Endemic and Ethnic Diseases , Ministry of Education, Guizhou Medical University, Guiyang, China * These authors contributed equally to this work. ABSTRACT Aim. Helicobacter pylori cytotoxin-associated protein A (CagA) is an important vir- ulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA- induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods. AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, | logFC |> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Submitted 20 August 2020 Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the Accepted 11 March 2021 key genes derived from the PPI network. Further analysis of the key gene expressions Published 15 April 2021 in gastric cancer and normal tissues were performed based on The Cancer Genome Corresponding author Atlas (TCGA) database and RT-qPCR verification. -
(Q36.1;Q24) with a Concurrent Submicroscopic Del(4)(Q23q24) in an Adult with Polycythemia Vera
cancers Case Report A Novel Acquired t(2;4)(q36.1;q24) with a Concurrent Submicroscopic del(4)(q23q24) in An Adult with Polycythemia Vera Eigil Kjeldsen Cancer Cytogenetic Section, HemoDiagnostic Laboratory, Department of Hematology, Aarhus University Hospital, Tage-Hansens Gade 2, DK-8000 Aarhus C, Denmark; [email protected]; Tel.: +45-7846-7398; Fax: +45-7846-7399 Received: 6 June 2018; Accepted: 21 June 2018; Published: 25 June 2018 Abstract: Background: Polycythemia vera (PV) is a clonal myeloid stem cell disease characterized by a growth-factor independent erythroid proliferation with an inherent tendency to transform into overt acute myeloid malignancy. Approximately 95% of the PV patients harbor the JAK2V617F mutation while less than 35% of the patients harbor cytogenetic abnormalities at the time of diagnosis. Methods and Results: Here we present a JAK2V617F positive PV patient where G-banding revealed an apparently balanced t(2;4)(q35;q21), which was confirmed by 24-color karyotyping. Oligonucleotide array-based Comparative Genomic Hybridization (aCGH) analysis revealed an interstitial 5.4 Mb large deletion at 4q23q24. Locus-specific fluorescent in situ hybridization (FISH) analyses confirmed the mono-allelic 4q deletion and that it was located on der(4)t(2;4). Additional locus-specific bacterial artificial chromosome (BAC) probes and mBanding refined the breakpoint on chromosome 2. With these methods the karyotype was revised to 46,XX,t(2;4)(q36.1;q24)[18]/46,XX[7]. Conclusions: This is the first report on a PV patient associated with an acquired novel t(2;4)(q36.1;q24) and a concurrent submicroscopic deletion del(4)(q23q24). -
INTS14 Form a Functional Module of Integrator That Binds Nucleic Acids and the Cleavage Module
ARTICLE https://doi.org/10.1038/s41467-020-17232-2 OPEN INTS10–INTS13–INTS14 form a functional module of Integrator that binds nucleic acids and the cleavage module Kevin Sabath1, Melanie L. Stäubli1, Sabrina Marti1, Alexander Leitner 2, Murielle Moes 1 & ✉ Stefanie Jonas 1 ′ 1234567890():,; The Integrator complex processes 3 -ends of spliceosomal small nuclear RNAs (snRNAs). Furthermore, it regulates transcription of protein coding genes by terminating transcription after unstable pausing. The molecular basis for Integrator’s functions remains obscure. Here, we show that INTS10, Asunder/INTS13 and INTS14 form a separable, functional Integrator module. The structure of INTS13-INTS14 reveals a strongly entwined complex with a unique chain interlink. Unexpected structural homology to the Ku70-Ku80 DNA repair complex suggests nucleic acid affinity. Indeed, the module displays affinity for DNA and RNA but prefers RNA hairpins. While the module plays an accessory role in snRNA maturation, it has a stronger influence on transcription termination after pausing. Asunder/INTS13 directly binds Integrator’s cleavage module via a conserved C-terminal motif that is involved in snRNA processing and required for spermatogenesis. Collectively, our data establish INTS10-INTS13- INTS14 as a nucleic acid-binding module and suggest that it brings cleavage module and target transcripts into proximity. 1 Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, CH-8093 Zurich, Switzerland. 2 Institute of Molecular Systems Biology, ETH ✉ Zurich, Zurich, Switzerland. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:3422 | https://doi.org/10.1038/s41467-020-17232-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-17232-2 NA polymerase II (RNAPII) requires essential processing development in Drosophila and Xenopus and shown to be factors to terminate transcription and to release its primary required for correct mitosis in human cells36,38,39. -
Gene Network Analysis in a Pediatric Cohort Identifies Novel Lung Function Genes
Gene Network Analysis in a Pediatric Cohort Identifies Novel Lung Function Genes Bruce A. Ong1.¤,JinLi2., Joseph M. McDonough1, Zhi Wei3, Cecilia Kim2, Rosetta Chiavacci2, Frank Mentch2, Jason B. Caboot4, Jonathan Spergel5,7,8, Julian L. Allen1,7, Patrick M. A. Sleiman2,6,7*, Hakon Hakonarson1,2,6,7* 1 Division of Pulmonary Medicine and Cystic Fibrosis Center, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 2 Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 3 Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America, 4 Division of Pediatric Pulmonology, Madigan Army Medical Center, Tacoma, Washington, United States of America, 5 Center for Pediatric Eosinophilic Disorders, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 6 Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 7 Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 8 Division of Allergy and Immunology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America Abstract Lung function is a heritable trait and serves as an important clinical predictor of morbidity and mortality for pulmonary conditions in adults, however, despite its importance, no studies have focused on uncovering pediatric-specific loci influencing lung function. To identify novel genetic determinants of pediatric lung function, we conducted a genome-wide association study (GWAS) of four pulmonary function traits, including FVC, FEV1, FEV1/FVC and FEF25–75% in 1556 children. -
IL-1 Transcriptional Responses to Lipopolysaccharides Are Regulated by a Complex of RNA Binding Proteins
IL-1 Transcriptional Responses to Lipopolysaccharides Are Regulated by a Complex of RNA Binding Proteins This information is current as Lihua Shi, Li Song, Kelly Maurer, Ying Dou, Vishesh R. of October 1, 2021. Patel, Chun Su, Michelle E. Leonard, Sumei Lu, Kenyaita M. Hodge, Annabel Torres, Alessandra Chesi, Struan F. A. Grant, Andrew D. Wells, Zhe Zhang, Michelle A. Petri and Kathleen E. Sullivan J Immunol published online 17 January 2020 http://www.jimmunol.org/content/early/2020/01/16/jimmun Downloaded from ol.1900650 Supplementary http://www.jimmunol.org/content/suppl/2020/01/16/jimmunol.190065 http://www.jimmunol.org/ Material 0.DCSupplemental Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on October 1, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2020 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published January 17, 2020, doi:10.4049/jimmunol.1900650 The Journal of Immunology IL-1 Transcriptional Responses to Lipopolysaccharides Are Regulated by a Complex of RNA Binding Proteins Lihua Shi,* Li Song,* Kelly Maurer,* Ying Dou,*,1 Vishesh R. -
M1BP Cooperates with CP190 to Activate Transcription at TAD Borders and Promote Chromatin Insulator Activity
ARTICLE https://doi.org/10.1038/s41467-021-24407-y OPEN M1BP cooperates with CP190 to activate transcription at TAD borders and promote chromatin insulator activity Indira Bag 1,2, Shue Chen 1,2,4, Leah F. Rosin 1,2,4, Yang Chen 1,2, Chen-Yu Liu3, Guo-Yun Yu3 & ✉ Elissa P. Lei 1,2 1234567890():,; Genome organization is driven by forces affecting transcriptional state, but the relationship between transcription and genome architecture remains unclear. Here, we identified the Drosophila transcription factor Motif 1 Binding Protein (M1BP) in physical association with the gypsy chromatin insulator core complex, including the universal insulator protein CP190. M1BP is required for enhancer-blocking and barrier activities of the gypsy insulator as well as its proper nuclear localization. Genome-wide, M1BP specifically colocalizes with CP190 at Motif 1-containing promoters, which are enriched at topologically associating domain (TAD) borders. M1BP facilitates CP190 chromatin binding at many shared sites and vice versa. Both factors promote Motif 1-dependent gene expression and transcription near TAD borders genome-wide. Finally, loss of M1BP reduces chromatin accessibility and increases both inter- and intra-TAD local genome compaction. Our results reveal physical and functional inter- action between CP190 and M1BP to activate transcription at TAD borders and mediate chromatin insulator-dependent genome organization. 1 Nuclear Organization and Gene Expression Section, Bethesda, MD, USA. 2 Laboratory of Biochemistry and Genetics, Bethesda, MD, USA. 3 Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA. ✉ 4These authors contributed equally: Shue Chen, Leah F. -
Proteomicsdb: a Multi-Omics and Multi-Organism Resource for Life Science
Published online 30 October 2019 Nucleic Acids Research, 2020, Vol. 48, Database issue D1153–D1163 doi: 10.1093/nar/gkz974 ProteomicsDB: a multi-omics and multi-organism resource for life science research Patroklos Samaras 1, Tobias Schmidt 1,MartinFrejno1, Siegfried Gessulat1,2, Maria Reinecke1,3,4, Anna Jarzab1, Jana Zecha1, Julia Mergner1, Piero Giansanti1, Hans-Christian Ehrlich2, Stephan Aiche2, Johannes Rank5,6, Harald Kienegger5,6, Helmut Krcmar5,6, Bernhard Kuster1,7,* and Mathias Wilhelm1,* 1Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany, 2Innovation Center Network, SAP SE, Potsdam, Germany, 3German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany, 4German Cancer Research Center (DKFZ), Heidelberg, Germany, 5Chair for Information Systems, Technical University of Munich (TUM), Garching, Germany, 6SAP University Competence Center, Technical University of Munich (TUM), Garching, Germany and 7Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich (TUM), Freising, Bavaria, Germany Received September 14, 2019; Revised October 11, 2019; Editorial Decision October 11, 2019; Accepted October 15, 2019 ABSTRACT INTRODUCTION ProteomicsDB (https://www.ProteomicsDB.org) ProteomicsDB (https://www.ProteomicsDB.org) is an in- started as a protein-centric in-memory database for memory database initially developed for the exploration of the exploration of large collections of quantitative large quantities of quantitative human mass spectrometry- mass spectrometry-based proteomics data. The based proteomics data including the first draft of the hu- data types and contents grew over time to include man proteome (1). Among many features, it allows the real- time exploration and retrieval of protein abundance values RNA-Seq expression data, drug-target interactions across different tissues, cell lines, and body fluids via inter- and cell line viability data. -
From Genome to Phenome
Dutch Society of Human Genetics (www.nvhg-nav.nl) From Genome to Phenome Two-Day Autumn Symposium 3 & 4 October 2013 Wij zijn bijzonder erkentelijk voor de financiële steun van Stichting Simonsfonds Logo’s: Tom de Vries Lentsch 2 Dear colleagues, Welcome to the joint autumn symposium of the NVHG (Dutch Society for Human Genetics). This meeting is organized together with the VKGL (Vereniging Klinisch Genetische Laboratoriumdiagnostiek), the VKGN (Vereniging Klinische Genetica Nederland) and the NACGG (Nederlandse Associatie voor Community Genetics en Public Health Genomics ) We live in an exciting time. Developments in genetics are going very fast. New technologies have advanced research and these findings are rapidly translated to diagnostics. Even non-invasive prenatal tests are now possible. Single gene tests with long turn-around times are being replaced by rapid analysis of gene panels and the genetic basis of multifactorial diseases is being resolved. This has great impact on the way we perform our research, diagnostics and counseling. Furthermore, we seriously have to reckon with the impact on society as illustrated by the recent publicity on the introduction of non-invasive prenatal testing. The scope of our profession is changing, interaction between the different professionals in genetics is increasingly important. This meeting is the perfect place to discuss the new developments and stimulate discussion of the possibilities and their implications. Thursday afternoon’s forum discussion on prenatal testing is a good example. This year we focus on the translation of the genetic findings to the patient. How do we interpret the sequence variants detected by the new technologies, which functional assays are available and how much functional evidence do we need before we start offering new tests? The scientific program covers basic research, disease models and implications for diagnostics.