Split-Turboid Enables Contact-Dependent Proximity Labeling in Cells
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ARMCX3 (NM 177948) Human Untagged Clone – SC124834
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 SC124834 ARMCX3 (NM_177948) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: ARMCX3 (NM_177948) Human Untagged Clone Tag: Tag Free Symbol: ARMCX3 Synonyms: ALEX3; dJ545K15.2; GASP6 Vector: pCMV6-XL4 E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: None Fully Sequenced ORF: >NCBI ORF sequence for NM_177948, the custom clone sequence may differ by one or more nucleotides ATGGGCTACGCCAGGAAAGTAGGCTGGGTGACCGCAGGCCTGGTGATTGGGGCTGGCGCCTGCTATTGCA TTTATAGACTGACTAGGGGAAGAAAACAGAACAAGGAAAAAATGGCTGAGGGTGGATCTGGGGATGTGGA TGATGCTGGGGACTGTTCTGGGGCCAGGTATAATGACTGGTCTGATGATGATGATGACAGCAATGAGAGC AAGAGTATAGTATGGTACCCACCTTGGGCTCGGATTGGGACTGAAGCTGGAACCAGAGCTAGGGCCAGGG CAAGGGCCAGGGCTACCCGGGCACGTCGGGCTGTCCAGAAACGGGCTTCCCCCAATTCAGATGATACCGT TTTGTCCCCTCAAGAGCTACAAAAGGTTCTTTGCTTGGTTGAGATGTCTGAAAAGCCTTATATTCTTGAA GCAGCTTTAATTGCTCTGGGTAACAATGCTGCTTATGCATTTAACAGAGATATTATTCGTGATCTGGGTG GTCTCCCAATTGTCGCAAAGATTCTCAATACTCGGGATCCCATAGTTAAGGAAAAGGCTTTAATTGTCCT GAATAACTTGAGTGTGAATGCTGAAAATCAGCGCAGGCTTAAAGTATACATGAATCAAGTGTGTGATGAC ACAATCACTTCTCGCTTGAACTCATCTGTGCAGCTTGCTGGACTGAGATTGCTTACAAATATGACTGTTA CTAATGAGTATCAGCACATGCTTGCTAATTCCATTTCTGACTTTTTTCGTTTATTTTCAGCGGGAAATGA AGAAACCAAACTTCAGGTTCTGAAACTCCTTTTGAATTTGGCTGAAAATCCAGCCATGACTAGGGAACTG CTCAGGGCCCAAGTACCATCTTCACTGGGCTCCCTCTTTAATAAGAAGGAGAACAAAGAAGTTATTCTTA AACTTCTGGTCATATTTGAGAACATAAATGATAATTTCAAATGGGAAGAAAATGAACCTACTCAGAATCA -
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. -
The Inactive X Chromosome Is Epigenetically Unstable and Transcriptionally Labile in Breast Cancer
Supplemental Information The inactive X chromosome is epigenetically unstable and transcriptionally labile in breast cancer Ronan Chaligné1,2,3,8, Tatiana Popova1,4, Marco-Antonio Mendoza-Parra5, Mohamed-Ashick M. Saleem5 , David Gentien1,6, Kristen Ban1,2,3,8, Tristan Piolot1,7, Olivier Leroy1,7, Odette Mariani6, Hinrich Gronemeyer*5, Anne Vincent-Salomon*1,4,6,8, Marc-Henri Stern*1,4,6 and Edith Heard*1,2,3,8 Extended Experimental Procedures Cell Culture Human Mammary Epithelial Cells (HMEC, Invitrogen) were grown in serum-free medium (HuMEC, Invitrogen). WI- 38, ZR-75-1, SK-BR-3 and MDA-MB-436 cells were grown in Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen) containing 10% fetal bovine serum (FBS). DNA Methylation analysis. We bisulfite-treated 2 µg of genomic DNA using Epitect bisulfite kit (Qiagen). Bisulfite converted DNA was amplified with bisulfite primers listed in Table S3. All primers incorporated a T7 promoter tag, and PCR conditions are available upon request. We analyzed PCR products by MALDI-TOF mass spectrometry after in vitro transcription and specific cleavage (EpiTYPER by Sequenom®). For each amplicon, we analyzed two independent DNA samples and several CG sites in the CpG Island. Design of primers and selection of best promoter region to assess (approx. 500 bp) were done by a combination of UCSC Genome Browser (http://genome.ucsc.edu) and MethPrimer (http://www.urogene.org). All the primers used are listed (Table S3). NB: MAGEC2 CpG analysis have been done with a combination of two CpG island identified in the gene core. Analysis of RNA allelic expression profiles (based on Human SNP Array 6.0) DNA and RNA hybridizations were normalized by Genotyping console. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Atlas of Subcellular RNA Localization Revealed by APEX-Seq
bioRxiv preprint doi: https://doi.org/10.1101/454470; this version posted October 30, 2018. 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. Atlas of Subcellular RNA Localization Revealed by APEX-seq Furqan M. Fazal1,2,3,*, Shuo Han3,4,5,*, Pornchai Kaewsapsak3,4,5, Kevin R. Parker1,2,3, Jin Xu1,2,3, Alistair N. Boettiger6, Howard Y. Chang1,2,3,7,†, Alice Y. Ting3,4,5,8,9,† 1Center for Personal Dynamics Regulomes, Stanford University School of Medicine, Stanford, CA, USA 2Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA 3Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA 4Department of Chemistry, Stanford University, Stanford, CA, USA 5Department of Biology, Stanford University, Stanford, CA, USA 6Department of Developmental Biology, Stanford University School of Medicine, CA, USA 7Howard Hughes Medical Institute 8Chan Zuckerberg Biohub, San Francisco, California, USA 9Lead contact *These authors contributed equally to this work †Corresponding authors. Email: [email protected] (A.Y.T.); [email protected] (H.Y.C.) SUMMARY We introduce APEX-seq, a method for RNA sequencing based on spatial proximity to the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome, revealing extensive and exquisite patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Mitoxplorer, a Visual Data Mining Platform To
mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, Cecilia Garcia-Perez, José Villaveces, Salma Gamal, Giovanni Cardone, Fabiana Perocchi, et al. To cite this version: Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, et al.. mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dy- namics and mutations. Nucleic Acids Research, Oxford University Press, 2020, 10.1093/nar/gkz1128. hal-02394433 HAL Id: hal-02394433 https://hal-amu.archives-ouvertes.fr/hal-02394433 Submitted on 4 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Nucleic Acids Research, 2019 1 doi: 10.1093/nar/gkz1128 Downloaded from https://academic.oup.com/nar/advance-article-abstract/doi/10.1093/nar/gkz1128/5651332 by Bibliothèque de l'université la Méditerranée user on 04 December 2019 mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim1,†, Prasanna Koti1,†, Adrien Bonnard2, Fabio Marchiano3, Milena Durrbaum¨ 1, Cecilia Garcia-Perez4, Jose Villaveces1, Salma Gamal1, Giovanni Cardone1, Fabiana Perocchi4, Zuzana Storchova1,5 and Bianca H. -
Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation Chip-Sequencing Data Processing Pipeline
Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Processing Pipeline Erinija Pranckeviciene Thesis submitted to the Faculty of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the Doctorate in Philosophy degree in Cellular and Molecular Medicine Department of Cellular and Molecular Medicine Faculty of Medicine University of Ottawa c Erinija Pranckeviciene, Ottawa, Canada, 2015 Abstract The rapidly advancing high-throughput and next generation sequencing technologies facilitate deeper insights into the molecular mechanisms underlying the expression of phenotypes in living organisms. Experimental data and scientific publications following this technological advance- ment have rapidly accumulated in public databases. Meaningful analysis of currently avail- able data in genomic databases requires sophisticated computational tools and algorithms, and presents considerable challenges to molecular biologists without specialized training in bioinfor- matics. To study their phenotype of interest molecular biologists must prioritize large lists of poorly characterized genes generated in high-throughput experiments. To date, prioritization tools have primarily been designed to work with phenotypes of human diseases as defined by the genes known to be associated with those diseases. There is therefore a need for more prioritiza- tion tools for phenotypes which are not related with diseases generally or diseases with which no genes have yet been associated in particular. Chromatin immunoprecipitation followed by next generation sequencing (ChIP-Seq) is a method of choice to study the gene regulation processes responsible for the expression of cellular phenotypes. Among publicly available computational pipelines for the processing of ChIP-Seq data, there is a lack of tools for the downstream analysis of composite motifs and preferred binding distances of the DNA binding proteins. -
Joint Profiling of Chromatin Accessibility and CAR-T Integration Site Analysis at Population and Single-Cell Levels
Joint profiling of chromatin accessibility and CAR-T integration site analysis at population and single-cell levels Wenliang Wanga,b,c,d, Maria Fasolinoa,b,c,d, Benjamin Cattaua,b,c,d, Naomi Goldmana,b,c,d, Weimin Konge,f,g, Megan A. Fredericka,b,c,d, Sam J. McCrighta,b,c,d, Karun Kiania,b,c,d, Joseph A. Fraiettae,f,g,h, and Golnaz Vahedia,b,c,d,f,1 aDepartment of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; bInstitute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; cEpigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; dInstitute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; eDepartment of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; fAbramson Family Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; gCenter for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104; and hParker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Edited by Anjana Rao, La Jolla Institute for Allergy and Immunology, La Jolla, CA, and approved January 30, 2020 (received for review November 3, 2019) Chimeric antigen receptor (CAR)-T immunotherapy has yielded tumor killing. To determine the extent to which these two sce- impressive results in several B cell malignancies, establishing itself narios occur in vivo, it is essential to simultaneously determine as a powerful means to redirect the natural properties of T lym- T cell fate and map where CAR-T vectors integrate into the phocytes. -
Mouse Parp16 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Parp16 Knockout Project (CRISPR/Cas9) Objective: To create a Parp16 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Parp16 gene (NCBI Reference Sequence: NM_177460 ; Ensembl: ENSMUSG00000032392 ) is located on Mouse chromosome 9. 7 exons are identified, with the ATG start codon in exon 2 and the TGA stop codon in exon 7 (Transcript: ENSMUST00000069000). Exon 3~5 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 18.12% of the coding region. Exon 3~5 covers 53.52% of the coding region. The size of effective KO region: ~7788 bp. The KO region does not have any other known gene. Page 1 of 9 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 3 4 5 7 Legends Exon of mouse Parp16 Knockout region Page 2 of 9 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 3 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section downstream of Exon 5 is aligned with itself to determine if there are tandem repeats. -
Ehrlichia Chaffeensis
RESEARCH ARTICLE Ehrlichia chaffeensis TRP47 enters the nucleus via a MYND-binding domain-dependent mechanism and predominantly binds enhancers of host genes associated with signal transduction, cytoskeletal organization, and immune response a1111111111 1 1 2 1 1 a1111111111 Clayton E. KiblerID , Sarah L. Milligan , Tierra R. Farris , Bing Zhu , Shubhajit Mitra , Jere a1111111111 W. McBride1,2,3,4,5* a1111111111 a1111111111 1 Department of Pathology, University of Texas Medical Branch, Galveston, Texas, United States of America, 2 Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, Texas, United States of America, 3 Center for Biodefense and Emerging Infectious Diseases, University of Texas Medical Branch, Galveston, Texas, United States of America, 4 Sealy Center for Vaccine Development, University of Texas Medical Branch, Galveston, Texas, United States of America, 5 Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, Texas, United States of OPEN ACCESS America Citation: Kibler CE, Milligan SL, Farris TR, Zhu B, * [email protected] Mitra S, McBride JW (2018) Ehrlichia chaffeensis TRP47 enters the nucleus via a MYND-binding domain-dependent mechanism and predominantly binds enhancers of host genes associated with Abstract signal transduction, cytoskeletal organization, and immune response. PLoS ONE 13(11): e0205983. Ehrlichia chaffeensis is an obligately intracellular bacterium that establishes infection in https://doi.org/10.1371/journal.pone.0205983 mononuclear phagocytes through largely undefined reprogramming strategies including Editor: Gary M. Winslow, State University of New modulation of host gene transcription. In this study, we demonstrate that the E. chaffeensis York Upstate Medical University, UNITED STATES effector TRP47 enters the host cell nucleus and binds regulatory regions of host genes rele- Received: April 25, 2018 vant to infection. -
Parps and ADP-Ribosylation: Recent Advances Linking Molecular Functions to Biological Outcomes
Downloaded from genesdev.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW PARPs and ADP-ribosylation: recent advances linking molecular functions to biological outcomes Rebecca Gupte,1,2 Ziying Liu,1,2 and W. Lee Kraus1,2 1Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA; 2Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA The discovery of poly(ADP-ribose) >50 years ago opened units derived from β-NAD+ to catalyze the ADP-ribosyla- a new field, leading the way for the discovery of the tion reaction. These enzymes include bacterial ADPRTs poly(ADP-ribose) polymerase (PARP) family of enzymes (e.g., cholera toxin and diphtheria toxin) as well as mem- and the ADP-ribosylation reactions that they catalyze. bers of three different protein families in yeast and ani- Although the field was initially focused primarily on the mals: (1) arginine-specific ecto-enzymes (ARTCs), (2) biochemistry and molecular biology of PARP-1 in DNA sirtuins, and (3) PAR polymerases (PARPs) (Hottiger damage detection and repair, the mechanistic and func- et al. 2010). Surprisingly, a recent study showed that the tional understanding of the role of PARPs in different bio- bacterial toxin DarTG can ADP-ribosylate DNA (Jankevi- logical processes has grown considerably of late. This has cius et al. 2016). How this fits into the broader picture of been accompanied by a shift of focus from enzymology to cellular ADP-ribosylation has yet to be determined.