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Ran Activation Assay Kit
Product Manual Ran Activation Assay Kit Catalog Number STA-409 20 assays FOR RESEARCH USE ONLY Not for use in diagnostic procedures Introduction Small GTP-binding proteins (or GTPases) are a family of proteins that serve as molecular regulators in signaling transduction pathways. Ran, a 25 kDa protein of the Ras superfamily, regulates a variety of biological response pathways that include DNA synthesis, cell cycle progression, and translocation of RNA/proteins through the nuclear pore complex. Like other small GTPases, Ran regulates molecular events by cycling between an inactive GDP-bound form and an active GTP-bound form. In its active (GTP-bound) state, Ran binds specifically to RanBP1 to control downstream signaling cascades. Cell Biolabs’ Ran Activation Assay Kit utilizes RanBP1 Agarose beads to selectively isolate and pull- down the active form of Ran from purified samples or endogenous lysates. Subsequently, the precipitated GTP-Ran is detected by western blot analysis using an anti-Ran antibody. Cell Biolabs’ Ran Activation Assay Kit provides a simple and fast tool to monitor the activation of Ran. The kit includes easily identifiable RanBP1 Agarose beads (see Figure 1), pink in color, and a GTPase Immunoblot Positive Control for quick Ran identification. Each kit provides sufficient quantities to perform 20 assays. Figure 1: RanBP1 Agarose beads, in color, are easy to visualize, minimizing potential loss during washes and aspirations. 2 Assay Principle Related Products 1. STA-400: Pan-Ras Activation Assay Kit 2. STA-400-H: H-Ras Activation Assay Kit 3. STA-400-K: K-Ras Activation Assay Kit 4. STA-400-N: N-Ras Activation Assay Kit 5. -
Characterization of Gf a Drosophila Trimeric G Protein Alpha Subunit
Characterization of Gf a Drosophila trimeric G protein alpha subunit Naureen Quibria Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2012 © 2012 Naureen Quibria All rights reserved Abstract Characterization of Gf a Drosophila trimeric G-protein alpha subunit Naureen Quibria In the morphogenesis of tissue development, how coordination of patterning and growth achieve the correct organ size and shape is a principal question in biology. Efficient orchestrating mechanisms are required to achieve this and cells have developed sophisticated systems for reception and interpretation of the multitude of extracellular stimuli to which they are exposed. Plasma membrane receptors play a key role in the transmission of such signals. G-protein coupled receptors (GPCRs) are the largest class of cell surface receptors that respond to an enormous diversity of extracellular stimuli, and are critical mediators of cellular signal transduction in eukaryotic organisms. Signaling through GPCRs has been well characterized in many biological contexts. While they are a major class of signal transducers, there are not many defined instances where GPCRs have been implicated in the process of development to date. The Drosophila wing provides an ideal model system to elucidate and address the role of GPCRs in development, as its growth is regulated by a small number of conserved signaling pathways. In my thesis work, I address the role of a trimeric G alpha protein in Drosophila, Gαf, and what part it may play in development. In particular, I explore the role of Gαf as an alpha subunit of a trimeric complex, to determine what heptahelical receptors might act as its cognate receptor. -
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. -
Hras Intracellular Trafficking and Signal Transduction Jodi Ho-Jung Mckay Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 2007 HRas intracellular trafficking and signal transduction Jodi Ho-Jung McKay Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Biological Phenomena, Cell Phenomena, and Immunity Commons, Cancer Biology Commons, Cell Biology Commons, Genetics and Genomics Commons, and the Medical Cell Biology Commons Recommended Citation McKay, Jodi Ho-Jung, "HRas intracellular trafficking and signal transduction" (2007). Retrospective Theses and Dissertations. 13946. https://lib.dr.iastate.edu/rtd/13946 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. HRas intracellular trafficking and signal transduction by Jodi Ho-Jung McKay A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Genetics Program of Study Committee: Janice E. Buss, Co-major Professor Linda Ambrosio, Co-major Professor Diane Bassham Drena Dobbs Ted Huiatt Iowa State University Ames, Iowa 2007 Copyright © Jodi Ho-Jung McKay, 2007. All rights reserved. UMI Number: 3274881 Copyright 2007 by McKay, Jodi Ho-Jung All rights reserved. UMI Microform 3274881 Copyright 2008 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. -
A SARS-Cov-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing Supplementary Information Supplementary Discussion All SARS-CoV-2 protein and gene functions described in the subnetwork appendices, including the text below and the text found in the individual bait subnetworks, are based on the functions of homologous genes from other coronavirus species. These are mainly from SARS-CoV and MERS-CoV, but when available and applicable other related viruses were used to provide insight into function. The SARS-CoV-2 proteins and genes listed here were designed and researched based on the gene alignments provided by Chan et. al. 1 2020 . Though we are reasonably sure the genes here are well annotated, we want to note that not every protein has been verified to be expressed or functional during SARS-CoV-2 infections, either in vitro or in vivo. In an effort to be as comprehensive and transparent as possible, we are reporting the sub-networks of these functionally unverified proteins along with the other SARS-CoV-2 proteins. In such cases, we have made notes within the text below, and on the corresponding subnetwork figures, and would advise that more caution be taken when examining these proteins and their molecular interactions. Due to practical limits in our sample preparation and data collection process, we were unable to generate data for proteins corresponding to Nsp3, Orf7b, and Nsp16. Therefore these three genes have been left out of the following literature review of the SARS-CoV-2 proteins and the protein-protein interactions (PPIs) identified in this study. -
New Targets of Urocortin-Mediated Cardioprotection
69 New targets of urocortin-mediated cardioprotection Sea´n P Barry1, Kevin M Lawrence4, James McCormick1, Surinder M Soond5, Mike Hubank2, Simon Eaton3, Ahila Sivarajah6, Tiziano M Scarabelli7, Richard A Knight1, Christoph Thiemermann6, David S Latchman1, Paul A Townsend8 and Anastasis Stephanou1 1Medical Molecular Biology Unit, 2Department of Molecular Haematology and 3Department of Surgery, Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK 4Department of Cellular Pathology, St George’s, University of London, Cranmer Terrace, Tooting, London, SW17 0RE, UK 5School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK 6St Bartholomew’s and The Royal London School of Medicine and Dentistry, William Harvey Research Institute, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London, EC1M 7BQ, UK 7Center for Heart and Vessel Preclinical Studies, St John Hospital and Medical Center, Wayne State University School of Medicine, 22201 Moross Road, Detroit, Michigan 48336, USA 8Human Genetics Division, MP808, Southampton General Hospital, University of Southampton, Southampton SO16 6YD, UK (Correspondence should be addressed to S P Barry; Email: [email protected]) Abstract The urocortin (UCN) hormones UCN1 and UCN2 have been shown previously to confer significant protection against myocardial ischaemia/reperfusion (I/R) injury; however, the molecular mechanisms underlying their action are poorly understood. To further define the transcriptional effect of UCNs that underpins their cardioprotective activity, a microarray analysis was carried out using an in vivo rat coronary occlusion model of I/R injury. Infusion of UCN1 or UCN2 before the onset of reperfusion resulted in the differential regulation of 66 and 141 genes respectively, the majority of which have not been described previously. -
High Throughput Strategies Aimed at Closing the GAP in Our Knowledge of Rho Gtpase Signaling
cells Review High Throughput strategies Aimed at Closing the GAP in Our Knowledge of Rho GTPase Signaling Manel Dahmene 1, Laura Quirion 2 and Mélanie Laurin 1,3,* 1 Oncology Division, CHU de Québec–Université Laval Research Center, Québec, QC G1V 4G2, Canada; [email protected] 2 Montréal Clinical Research Institute (IRCM), Montréal, QC H2W 1R7, Canada; [email protected] 3 Université Laval Cancer Research Center, Québec, QC G1R 3S3, Canada * Correspondence: [email protected] Received: 21 May 2020; Accepted: 7 June 2020; Published: 9 June 2020 Abstract: Since their discovery, Rho GTPases have emerged as key regulators of cytoskeletal dynamics. In humans, there are 20 Rho GTPases and more than 150 regulators that belong to the RhoGEF, RhoGAP, and RhoGDI families. Throughout development, Rho GTPases choregraph a plethora of cellular processes essential for cellular migration, cell–cell junctions, and cell polarity assembly. Rho GTPases are also significant mediators of cancer cell invasion. Nevertheless, to date only a few molecules from these intricate signaling networks have been studied in depth, which has prevented appreciation for the full scope of Rho GTPases’ biological functions. Given the large complexity involved, system level studies are required to fully grasp the extent of their biological roles and regulation. Recently, several groups have tackled this challenge by using proteomic approaches to map the full repertoire of Rho GTPases and Rho regulators protein interactions. These studies have provided in-depth understanding of Rho regulators specificity and have contributed to expand Rho GTPases’ effector portfolio. Additionally, new roles for understudied family members were unraveled using high throughput screening strategies using cell culture models and mouse embryos. -
Supplementary Table 9. Functional Annotation Clustering Results for the Union (GS3) of the Top Genes from the SNP-Level and Gene-Based Analyses (See ST4)
Supplementary Table 9. Functional Annotation Clustering Results for the union (GS3) of the top genes from the SNP-level and Gene-based analyses (see ST4) Column Header Key Annotation Cluster Name of cluster, sorted by descending Enrichment score Enrichment Score EASE enrichment score for functional annotation cluster Category Pathway Database Term Pathway name/Identifier Count Number of genes in the submitted list in the specified term % Percentage of identified genes in the submitted list associated with the specified term PValue Significance level associated with the EASE enrichment score for the term Genes List of genes present in the term List Total Number of genes from the submitted list present in the category Pop Hits Number of genes involved in the specified term (category-specific) Pop Total Number of genes in the human genome background (category-specific) Fold Enrichment Ratio of the proportion of count to list total and population hits to population total Bonferroni Bonferroni adjustment of p-value Benjamini Benjamini adjustment of p-value FDR False Discovery Rate of p-value (percent form) Annotation Cluster 1 Enrichment Score: 3.8978262119731335 Category Term Count % PValue Genes List Total Pop Hits Pop Total Fold Enrichment Bonferroni Benjamini FDR GOTERM_CC_DIRECT GO:0005886~plasma membrane 383 24.33290978 5.74E-05 SLC9A9, XRCC5, HRAS, CHMP3, ATP1B2, EFNA1, OSMR, SLC9A3, EFNA3, UTRN, SYT6, ZNRF2, APP, AT1425 4121 18224 1.18857065 0.038655922 0.038655922 0.086284383 UP_KEYWORDS Membrane 626 39.77128335 1.53E-04 SLC9A9, HRAS, -
Transcriptomic Uniqueness and Commonality of the Ion Channels and Transporters in the Four Heart Chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A
www.nature.com/scientificreports OPEN Transcriptomic uniqueness and commonality of the ion channels and transporters in the four heart chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A. Iacobas3,4* Myocardium transcriptomes of left and right atria and ventricles from four adult male C57Bl/6j mice were profled with Agilent microarrays to identify the diferences responsible for the distinct functional roles of the four heart chambers. Female mice were not investigated owing to their transcriptome dependence on the estrous cycle phase. Out of the quantifed 16,886 unigenes, 15.76% on the left side and 16.5% on the right side exhibited diferential expression between the atrium and the ventricle, while 5.8% of genes were diferently expressed between the two atria and only 1.2% between the two ventricles. The study revealed also chamber diferences in gene expression control and coordination. We analyzed ion channels and transporters, and genes within the cardiac muscle contraction, oxidative phosphorylation, glycolysis/gluconeogenesis, calcium and adrenergic signaling pathways. Interestingly, while expression of Ank2 oscillates in phase with all 27 quantifed binding partners in the left ventricle, the percentage of in-phase oscillating partners of Ank2 is 15% and 37% in the left and right atria and 74% in the right ventricle. The analysis indicated high interventricular synchrony of the ion channels expressions and the substantially lower synchrony between the two atria and between the atrium and the ventricle from the same side. Starting with crocodilians, the heart pumps the blood through the pulmonary circulation and the systemic cir- culation by the coordinated rhythmic contractions of its upper lef and right atria (LA, RA) and lower lef and right ventricles (LV, RV). -
Identification of Motifs in Cholera Toxin A1 Polypeptide That Are Required for Its Interaction with Human ADP-Ribosylation Factor 6 in a Bacterial Two-Hybrid System
Identification of motifs in cholera toxin A1 polypeptide that are required for its interaction with human ADP-ribosylation factor 6 in a bacterial two-hybrid system Michael G. Jobling and Randall K. Holmes* Department of Microbiology, University of Colorado Health Sciences Center, Denver, CO 80220 Edited by John J. Mekalanos, Harvard Medical School, Boston, MA, and approved October 20, 2000 (received for review September 14, 2000) The latent ADP-ribosyltransferase activity of cholera toxin (CT) that teins called ADP-ribosylation factors (ARFs; refs. 5 and 6). is activated after proteolytic nicking and reduction is associated ARFs are members of a highly conserved multigene family of with the CT A1 subunit (CTA1) polypeptide. This activity is stimu- small GTP-binding proteins, originally identified as activators of lated in vitro by interaction with eukaryotic proteins termed CT. They interact with several other proteins and are ubiqui- ADP-ribosylation factors (ARFs). We analyzed this interaction in a tously involved in membrane trafficking events (7). ARFs must modified bacterial two-hybrid system in which the T18 and T25 be in the GTP-bound form to be active. There are six mammalian fragments of the catalytic domain of Bordetella pertussis adenylate ARFs (five human) that fall into three classes: ARFs 1, 2, and cyclase were fused to CTA1 and human ARF6 polypeptides, respec- 3 (class I), ARFs 4 and 5 (class II), and ARF6 (class III). Class tively. Direct interaction between the CTA1 and ARF6 domains in I ARFs regulate the assembly of several types of vesicle coat these hybrid proteins reconstituted the adenylate cyclase activity complexes (8). -
MALE Protein Name Accession Number Molecular Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean H Mean PDAC Mean T-Test PDAC Vs. H T-Test
MALE t-test t-test Accession Molecular H PDAC PDAC vs. PDAC vs. Protein Name Number Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean Mean Mean H CP PDAC/H PDAC/CP - 22 kDa protein IPI00219910 22 kDa 7 5 4 8 1 0 6 6 1 0.1126 0.0456 0.1 0.1 - Cold agglutinin FS-1 L-chain (Fragment) IPI00827773 12 kDa 32 39 34 26 53 57 36 30 55 0.0309 0.0388 1.8 1.5 - HRV Fab 027-VL (Fragment) IPI00827643 12 kDa 4 6 0 0 0 0 5 0 0 - 0.0574 - 0.0 - REV25-2 (Fragment) IPI00816794 15 kDa 8 12 5 7 8 9 10 6 8 0.2225 0.3844 1.3 0.8 A1BG Alpha-1B-glycoprotein precursor IPI00022895 54 kDa 115 109 106 112 111 100 112 109 105 0.6497 0.4138 1.0 0.9 A2M Alpha-2-macroglobulin precursor IPI00478003 163 kDa 62 63 86 72 14 18 63 79 16 0.0120 0.0019 0.2 0.3 ABCB1 Multidrug resistance protein 1 IPI00027481 141 kDa 41 46 23 26 52 64 43 25 58 0.0355 0.1660 2.4 1.3 ABHD14B Isoform 1 of Abhydrolase domain-containing proteinIPI00063827 14B 22 kDa 19 15 19 17 15 9 17 18 12 0.2502 0.3306 0.7 0.7 ABP1 Isoform 1 of Amiloride-sensitive amine oxidase [copper-containing]IPI00020982 precursor85 kDa 1 5 8 8 0 0 3 8 0 0.0001 0.2445 0.0 0.0 ACAN aggrecan isoform 2 precursor IPI00027377 250 kDa 38 30 17 28 34 24 34 22 29 0.4877 0.5109 1.3 0.8 ACE Isoform Somatic-1 of Angiotensin-converting enzyme, somaticIPI00437751 isoform precursor150 kDa 48 34 67 56 28 38 41 61 33 0.0600 0.4301 0.5 0.8 ACE2 Isoform 1 of Angiotensin-converting enzyme 2 precursorIPI00465187 92 kDa 11 16 20 30 4 5 13 25 5 0.0557 0.0847 0.2 0.4 ACO1 Cytoplasmic aconitate hydratase IPI00008485 98 kDa 2 2 0 0 0 0 2 0 0 - 0.0081 - 0.0 -
1 No. Affymetrix ID Gene Symbol Genedescription Gotermsbp Q Value 1. 209351 at KRT14 Keratin 14 Structural Constituent of Cyto
1 Affymetrix Gene Q No. GeneDescription GOTermsBP ID Symbol value structural constituent of cytoskeleton, intermediate 1. 209351_at KRT14 keratin 14 filament, epidermis development <0.01 biological process unknown, S100 calcium binding calcium ion binding, cellular 2. 204268_at S100A2 protein A2 component unknown <0.01 regulation of progression through cell cycle, extracellular space, cytoplasm, cell proliferation, protein kinase C inhibitor activity, protein domain specific 3. 33323_r_at SFN stratifin/14-3-3σ binding <0.01 regulation of progression through cell cycle, extracellular space, cytoplasm, cell proliferation, protein kinase C inhibitor activity, protein domain specific 4. 33322_i_at SFN stratifin/14-3-3σ binding <0.01 structural constituent of cytoskeleton, intermediate 5. 201820_at KRT5 keratin 5 filament, epidermis development <0.01 structural constituent of cytoskeleton, intermediate 6. 209125_at KRT6A keratin 6A filament, ectoderm development <0.01 regulation of progression through cell cycle, extracellular space, cytoplasm, cell proliferation, protein kinase C inhibitor activity, protein domain specific 7. 209260_at SFN stratifin/14-3-3σ binding <0.01 structural constituent of cytoskeleton, intermediate 8. 213680_at KRT6B keratin 6B filament, ectoderm development <0.01 receptor activity, cytosol, integral to plasma membrane, cell surface receptor linked signal transduction, sensory perception, tumor-associated calcium visual perception, cell 9. 202286_s_at TACSTD2 signal transducer 2 proliferation, membrane <0.01 structural constituent of cytoskeleton, cytoskeleton, intermediate filament, cell-cell adherens junction, epidermis 10. 200606_at DSP desmoplakin development <0.01 lectin, galactoside- sugar binding, extracellular binding, soluble, 7 space, nucleus, apoptosis, 11. 206400_at LGALS7 (galectin 7) heterophilic cell adhesion <0.01 2 S100 calcium binding calcium ion binding, epidermis 12. 205916_at S100A7 protein A7 (psoriasin 1) development <0.01 S100 calcium binding protein A8 (calgranulin calcium ion binding, extracellular 13.