DOCSLIB.ORG

Transcriptional Regulation and Biological Function of the RNA

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Transcriptional Regulation and Biological Function of the RNA Regulation and biological function of the RNA ligase RtcB in Escherichia coli Georgia Mary Hann Centre for Bacterial Cell Biology Institute for Cell and Molecular Biosciences Thesis submitted for the degree Doctor of Philosophy August 2017 Abstract All organisms contain RNA ligases for the modification or repair of RNA molecules. The RtcB family have a novel 3ʹ-5ʹ RNA ligase activity and are conserved in all three domains of life. Metazoan homologues of RtcB have essential roles in tRNA maturation and the unfolded protein response. In E. coli, expression of RtcB from the σ54-dependent rtcBA operon is regulated by transcriptional activator RtcR. Neither the signals that induce rtcB expression nor the natural substrates of RtcB in E. coli are known. The objective of this project is to understand the regulation of rtcB expression by RtcR and the biological function of RtcB in E. coli. In this study, we confirm the roles of RtcR and σ54 in rtcB transcription in E. coli. Using a series of truncated rtcBA promoter fusions with the reporter gene lacZ, we have identified an inverted repeat that we hypothesise is the RtcR binding site in the promoter of rtcB. Mutation of the inverted repeat supports its involvement in RtcR binding. Using a similar approach, we have identified a possible σ70 promoter responsible for transcription of rtcR and provide evidence of negative autoregulation of rtcR. Moreover, we also demonstrate for the first time the requirement of IHF for RtcR-dependent transcription of rtcB and confirm the putative binding site of IHF in the rtcBA promoter in vivo and in vitro. Using a combination of genetic screening with a chromosomal rtcBA-lacZ fusion and quantitative PCR analysis, we have uncovered several conditions where transcription of rtcB is activated in E. coli. Expression of Rof, an inhibitor of Rho transcription termination, induced rtcB transcription in an RtcR-dependent manner, indicating a possible link between RtcB function and disruption of transcription termination. Furthermore, treatment of cells with chloramphenicol and induction of endoribonuclease MazF increased rtcB transcription, suggesting that RtcB may respond to the translational status of the cell. We have used these conditions to perform CRAC (crosslinking and analysis of cDNA) of RtcB, in order to identify substrate RNAs that interact with RtcB in vivo. Many interactions between RtcB and RNAs were identified including rRNAs, tRNAs, sRNAs and mRNAs and several candidates were further analysed by qPCR. Taken together, the data suggests that rtcB has features typical of a σ54-dependent promoter and that RtcB expression can be activated in a variety of stress conditions, thereby allowing interaction with a range of cellular RNAs in E. coli. Nevertheless, the specific signal(s) detected by RtcR and the biological function of RtcB in E. coli remain elusive. i Acknowledgements I would like to thank my supervisor Kenn Gerdes for the opportunity to work on this project and for help and guidance throughout. I also thank Kristoffer Winther for useful scientific discussions and help with experimental procedures. I am grateful to Kathryn Turnbull for many useful discussions, scientific and otherwise. I also acknowledge and thank past and present members of the Gerdes group for their support, technical expertise and suggestions. I am grateful to members of the Centre for Bacterial Cell Biology, Newcastle University and the Centre for Bacterial Stress Response and Persistence, University of Copenhagen for providing a welcoming and stimulating work environment. I thank Michael Sørensen and his group (University of Copenhagen) for an interesting collaboration. Finally, I thank Mick Hollier, Susan Hann and David Hann for their constant love, encouragement and moral support. ii Contents Abstract .................................................................................................................................... i Acknowledgements ................................................................................................................. ii Contents ................................................................................................................................. iii List of figures ........................................................................................................................ vii List of tables ........................................................................................................................... ix Abbreviations .......................................................................................................................... x Chapter 1 Introduction ....................................................................................................... 1 1.1 Overview ...................................................................................................................... 1 1.2 Promoter recognition in bacterial transcription ........................................................... 2 1.2.1 Bacterial transcription and the RNA polymerase holoenzyme ............................ 2 1.2.2 The σ factor of RNA polymerase holoenzyme ..................................................... 3 1.3 Sigma 54-dependent transcription ............................................................................... 5 1.3.1 Mode of transcription initiation by σ54-associated RNAP .................................... 5 1.3.2 Bacterial enhancer binding proteins ..................................................................... 8 1.3.2.1 ATPase domain of bEBPs ............................................................................. 8 1.3.2.2 Regulatory domain of bEBPs ...................................................................... 10 1.3.2.3 DNA binding domain of bEBPs .................................................................. 11 1.3.3 The role of integration host factor in σ54-dependent transcription ..................... 13 1.3.4 The σ54 regulon ................................................................................................... 14 1.4 Sigma 54-dependent transcription of the rtcBA operon in E. coli ............................. 14 1.4.1 Features of the rtcBA promoter .......................................................................... 14 1.4.2 RtcR, the bEBP of the rtcBA operon .................................................................. 15 1.5 The RNA ligase RtcB ................................................................................................ 17 1.5.1 Types and functions of RNA ligases .................................................................. 17 1.5.1.1 5ʹ-3ʹ RNA ligases ........................................................................................ 17 1.5.1.2 2ʹ-5ʹ RNA ligases ........................................................................................ 19 1.5.1.3 3ʹ-5ʹ RNA ligases ........................................................................................ 20 iii 1.5.2 Mechanism of action of RtcB ............................................................................. 20 1.5.3 Additional activities of RtcB in DNA modification ........................................... 24 1.6 Function and conservation of RtcB ........................................................................... 24 1.6.1 Members of the RtcB family are present in all domains of life ......................... 24 1.6.2 Function in eukaryotes and archaea.................................................................... 25 1.6.2.1 tRNA maturation ......................................................................................... 25 1.6.2.2 The unfolded protein response .................................................................... 27 1.6.2.3 Other indications of function ...................................................................... 29 1.6.3 Function in bacteria ............................................................................................ 32 1.6.4 Genes commonly associated with rtcB in bacteria ............................................. 34 1.6.4.1 The RNA cyclase RtcA ............................................................................... 34 1.6.4.2 Ro, an RNA scavenger ................................................................................ 36 1.6.4.3 The RtcB cofactor archease......................................................................... 37 1.7 Aims of this work ...................................................................................................... 38 Chapter 2 Materials and methods .................................................................................... 39 2.1 Bacterial strains, media and growth conditions ......................................................... 39 2.2 Standard DNA procedures ......................................................................................... 39 2.3 Strain construction ..................................................................................................... 46 2.4 Plasmid construction .................................................................................................. 50 2.4.1 Transcriptional fusions ....................................................................................... 50 2.4.2 Overexpression of rtc genes ............................................................................... 55 2.4.3 Expression of tagged rtc genes ........................................................................... 56 2.4.4 Others ................................................................................................................
Load more

Recommended publications
  • 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.
    [Show full text]
  • Thèse De Doctorat De L'université Paris-Saclay Préparée À L'ecole
    1 NNT : 2017SACLX031 Thèse de doctorat de l’Université Paris-Saclay préparée à l’Ecole Polytechnique Ecole doctorale n◦573 Interfaces : approches interdisciplinaires / fondements, applications et innovation Spécialité de doctorat : Informatique par Mme. Alice Héliou Analyse des séquences génomiques : Identification des ARNs circulaires et calcul de l’information négative Thèse présentée et soutenue à l’Ecole Polytechnique, le 10 juillet 2017. Composition du Jury : M. Alain Denise Professeur (Président du jury) Université Paris-Sud Mme. Christine Gaspin Professeur (Rapporteur) INRA Toulouse M. Gabriele Fici Professeur associé (Rapporteur) Université de Palerme (Italie) M Thierry Lecroq Professeur (Examinateur) Université de Rouen Mme. Claire Toffano-Nioche Chargé de recherche (Examinatrice) Université Paris Sud M. Mikael Salson Maitre de conférences (Examinateur) Université Lille 1 Mme. Mireille Régnier Professeur (Directrice de thèse) Ecole Polytechnique M. Hubert Becker Maitre de conférences (Co-directeur de thèse) UPMC À mes grands parents, Pour m’avoir ouvert les yeux sur la chance que sont les études. À mes parents, Pour m’avoir toujours soutenue et donné confiance en moi, Merci. Remerciements Je tiens dans un premier temps à remercier mes directeurs de thèse, Mireille Régnier et Hubert Becker sans qui rien de tout cela n’aurait été possible. Christine Gaspin et Gabriele Fici ont accepté d’être rapporteurs de cette thèse, je les remercie sincèrement d’avoir pris le temps de lire attentivement le manuscrit. Je souhaite également remercier Alain Denise, Thierry Lecroq, Claire Toffano- Nioche et Mikael Salson d’avoir accepté de faire partie de mon jury pour la soute- nance. La première personne qu’il m’est important de remercier est Laurent Mouchard.
    [Show full text]
  • Product Size GOT1 P00504 F CAAGCTGT
    Table S1. List of primer sequences for RT-qPCR. Gene Product Uniprot ID F/R Sequence(5’-3’) name size GOT1 P00504 F CAAGCTGTCAAGCTGCTGTC 71 R CGTGGAGGAAAGCTAGCAAC OGDHL E1BTL0 F CCCTTCTCACTTGGAAGCAG 81 R CCTGCAGTATCCCCTCGATA UGT2A1 F1NMB3 F GGAGCAAAGCACTTGAGACC 93 R GGCTGCACAGATGAACAAGA GART P21872 F GGAGATGGCTCGGACATTTA 90 R TTCTGCACATCCTTGAGCAC GSTT1L E1BUB6 F GTGCTACCGAGGAGCTGAAC 105 R CTACGAGGTCTGCCAAGGAG IARS Q5ZKA2 F GACAGGTTTCCTGGCATTGT 148 R GGGCTTGATGAACAACACCT RARS Q5ZM11 F TCATTGCTCACCTGCAAGAC 146 R CAGCACCACACATTGGTAGG GSS F1NLE4 F ACTGGATGTGGGTGAAGAGG 89 R CTCCTTCTCGCTGTGGTTTC CYP2D6 F1NJG4 F AGGAGAAAGGAGGCAGAAGC 113 R TGTTGCTCCAAGATGACAGC GAPDH P00356 F GACGTGCAGCAGGAACACTA 112 R CTTGGACTTTGCCAGAGAGG Table S2. List of differentially expressed proteins during chronic heat stress. score name Description MW PI CC CH Down regulated by chronic heat stress A2M Uncharacterized protein 158 1 0.35 6.62 A2ML4 Uncharacterized protein 163 1 0.09 6.37 ABCA8 Uncharacterized protein 185 1 0.43 7.08 ABCB1 Uncharacterized protein 152 1 0.47 8.43 ACOX2 Cluster of Acyl-coenzyme A oxidase 75 1 0.21 8 ACTN1 Alpha-actinin-1 102 1 0.37 5.55 ALDOC Cluster of Fructose-bisphosphate aldolase 39 1 0.5 6.64 AMDHD1 Cluster of Uncharacterized protein 37 1 0.04 6.76 AMT Aminomethyltransferase, mitochondrial 42 1 0.29 9.14 AP1B1 AP complex subunit beta 103 1 0.15 5.16 APOA1BP NAD(P)H-hydrate epimerase 32 1 0.4 8.62 ARPC1A Actin-related protein 2/3 complex subunit 42 1 0.34 8.31 ASS1 Argininosuccinate synthase 47 1 0.04 6.67 ATP2A2 Cluster of Calcium-transporting
    [Show full text]
  • Inferring Edge Function in Protein-Protein Interaction Networks
    bioRxiv preprint doi: https://doi.org/10.1101/321984; this version posted May 18, 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 4.0 International license. Bioinformatics, YYYY, 0–0 doi: 10.1093/bioinformatics/xxxxx Advance Access Publication Date: DD Month YYYY Manuscript Category Systems Biology Inferring edge function in protein-protein interaction networks Daniel Esposito1, Joseph Cursons1,2, Melissa Davis1-3,* 1 Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia 2 Department of Medical Biology, Faculty of Medical and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia 3 Department of Biochemistry and Molecular Biology, Faculty of Medicine, Dentistry and Health, University of Melbourne, VIC 3010, Australia. *To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Motivation: Post-translational modifications (PTMs) regulate many key cellular processes. Numerous studies have linked the topology of protein-protein interaction (PPI) networks to many biological phenomena such as key regulatory processes and disease. However, these methods fail to give insight in the functional nature of these interactions. On the other hand, pathways are commonly used to gain biological insight into the function of PPIs in the context of cascading interactions, sacrificing the coverage of networks for rich functional annotations on each PPI. We present a machine learning approach that uses Gene Ontology, InterPro and Pfam annotations to infer the edge functions in PPI networks, allowing us to combine the high coverage of networks with the information richness of pathways.
    [Show full text]
  • Downloaded a Meta
    bioRxiv preprint doi: https://doi.org/10.1101/2020.07.18.210328; this version posted April 12, 2021. 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. Dynamic regulatory module networks for inference of cell type-specific transcriptional networks Alireza Fotuhi Siahpirani1,2,+, Sara Knaack1+, Deborah Chasman1,8,+, Morten Seirup3,4, Rupa Sridharan1,5, Ron Stewart3, James Thomson3,5,6, and Sushmita Roy1,2,7* 1Wisconsin Institute for Discovery, University of Wisconsin-Madison 2Department of Computer Sciences, University of Wisconsin-Madison 3Morgridge Institute for Research 4Molecular and Environmental Toxicology Program, University of Wisconsin-Madison 5Department of Cell and Regenerative Biology, University of Wisconsin-Madison 6Department of Molecular, Cellular, & Developmental Biology, University of California Santa Barbara 7Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison 8Present address: Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Wisconsin-Madison +These authors contributed equally. *To whom correspondence should be addressed. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.18.210328; this version posted April 12, 2021. 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. Abstract Multi-omic datasets with parallel transcriptomic and epigenomic measurements across time or cell types are becoming increasingly common.
    [Show full text]
  • Bioinformatic Analysis of the Expression and Prognostic Value Of
    www.nature.com/scientificreports OPEN Bioinformatic analysis of the expression and prognostic value of chromobox family proteins in human breast cancer Xiaomin Li1,3, Junhe Gou2, Hongjiang Li3 & Xiaoqin Yang3* Chromobox (CBX) family proteins control chromatin structure and gene expression. However, the functions of CBXs in cancer progression, especially breast cancer, are inadequately studied. We assessed the signifcance of eight CBX proteins in breast cancer. We performed immunohistochemistry and bioinformatic analysis of data from Oncomine, GEPIA Dataset, bcGenExMiner, Kaplan–Meier Plotter, and cBioPortal. We compared mRNA and protein expression levels of eight CBX proteins between breast tumor and normal tissue. The expression diference of CBX7 was the greatest, and CBX7 was downregulated in breast cancer tissues compared with normal breast tissues. The expression of CBX2 was strongly associated with tumor stage. We further analyzed the association between the eight CBX proteins and the following clinicopathological features: menopause age, estrogen receptor (ER), progesterone receptor (PR) and HER-2 receptor status, nodal status, P53 status, triple-negative status, and the Scarf–Bloom–Richardson grade (SBR) and Nottingham prognostic index (NPI). Survival analysis in the Kaplan–Meier Plotter database showed that the eight CBX proteins were signifcantly associated with prognosis. Moreover, CBX genes in breast cancer patients had a high net alteration frequency of 57%. There were signifcant co-expression correlations between the following CBX protein pairs: CBX4 positively with CBX8, CBX6 positively with CBX7, and CBX2 negatively with CBX7. We also analyzed the Gene Ontology enrichment of the CBX proteins, including biological processes, cellular components, and molecular functions. CBX 1/2/3/5/8 may be oncogenes for breast cancer, whereas CBX 6 and 7 may be tumor suppressors for breast cancer.
    [Show full text]
  • Crystal Structure of an Rtcb Homolog Protein (PH1602-Extein Protein) Frompyrococcus Horikoshii Reveals a Novel Fold
    Title Crystal structure of an RtcB homolog protein (PH1602-extein protein) fromPyrococcus horikoshii reveals a novel fold Author(s) Okada, Chiaki; Maegawa, Yuki; Yao, Min; Tanaka, Isao Proteins: Structure Function and Bioinformatics, 63(4), 1119-1122 Citation https://doi.org/10.1002/prot.20912 Issue Date 2006-06 Doc URL http://hdl.handle.net/2115/14639 Rights Copyright (c) 2006 Wiley-Liss, Inc. Type article (author version) File Information for_HUSCAP.pdf Instructions for use Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP Crystal structure of an RtcB homolog protein (PH1602-extein protein) from Pyrococcus horikoshii reveals a novel fold Chiaki Okada, Yuki Maegawa, Min Yao, Isao Tanaka * Division of Biological Sciences, Graduate School of Science, Hokkaido University, Sapporo 060-0810, Japan email: Isao Tanaka ([email protected]) *Correspondence to Isao Tanaka, Division of Biological Sciences, Graduate School of Science, Hokkaido University, Sapporo 060-0810, Japan Grant sponsor: National Project on Protein Structural and Functional Analyses from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. Introduction. The hypothetical extein, PH1602-extein (53.5 kDa, 481 residues) from the hyperthermophilic archaebacterium, Pyrococcus horikoshii OT3, shows sequence similarity to Escherichia coli RtcB (33% identity). RtcB homologs are conserved in all archaebacteria, in most eukaryotes, including metazoa, and in a wide variety of eubacteria. In E. coli, the rtcB gene together with rtcA and rtcR comprise the σ54-dependent operon termed the RNA 3'-terminal phosphate cyclase operon1. The functions of the gene products RtcA and RtcR have been characterized previously as RNA 3'-terminal phosphate cyclase and σ54-specific regulator, respectively.
    [Show full text]
  • RNA Ligation Precedes the Retrotransposition of U6/LINE-1 Chimeric RNA
    RNA ligation precedes the retrotransposition of U6/LINE-1 chimeric RNA John B. Moldovana,1, Yifan Wanga,b, Stewart Shumanc, Ryan E. Millsa,b, and John V. Morana,d,1 aDepartment of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109; bDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109; cMolecular Biology Program, Sloan Kettering Institute, New York, NY 10065; and dDepartment of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109 Edited by Marlene Belfort, University at Albany, Albany, NY, and approved August 29, 2019 (received for review March 28, 2018) Long interspersed element-1 (LINE-1 or L1) amplifies via retrotrans- phosphate (45). The 2′,3′-cyclic phosphate is generated post- position. Active L1s encode 2 proteins (ORF1p and ORF2p) that bind transcriptionally by the Mpn1 enzyme (46, 47), which is encoded their encoding transcript to promote retrotransposition in cis.The by the U6 snRNA biogenesis phosphodiesterase 1 (USB1)gene. L1-encoded proteins also promote the retrotransposition of small- Deletions or mutations in USB1 disrupt U6 snRNA 3′ end pro- interspersed element RNAs, noncoding RNAs, and messenger RNAs cessing (46, 47) and are associated with the human genetic disease in trans. Some L1-mediated retrotransposition events consist of poikiloderma with neutropenia (48). a copy of U6 RNA conjoined to a variably 5′-truncated L1, but how The L1 proteins can act in trans to promote the retrotrans- U6/L1 chimeras are formed requires elucidation. Here, we report the position of a variety of cellular RNAs, including, small inter- following: The RNA ligase RtcB can join U6 RNAs ending in a 2′,3′- spersed element (SINE) RNAs (49–52), noncoding RNAs ′ cyclic phosphate to L1 RNAs containing a 5 -OH in vitro; depletion of (53–56), and messenger RNAs (30, 31).
    [Show full text]
  • Phosphate Cyclase Bound to Substrate RNA
    REPORT Structure of RNA 3′-phosphate cyclase bound to substrate RNA KEVIN K. DESAI,1 CRAIG A. BINGMAN,1 CHIN L. CHENG,1 GEORGE N. PHILLIPS JR.,1,2 and RONALD T. RAINES1,3 1Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706, USA 2BioSciences at Rice and Department of Chemistry, Rice University, Houston, Texas 77005, USA 3Department of Chemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706, USA ABSTRACT RNA 3′-phosphate cyclase (RtcA) catalyzes the ATP-dependent cyclization of a 3′-phosphate to form a 2′,3′-cyclic phosphate at RNA termini. Cyclization proceeds through RtcA–AMP and RNA(3′)pp(5′)A covalent intermediates, which are analogous to intermediates formed during catalysis by the tRNA ligase RtcB. Here we present a crystal structure of Pyrococcus horikoshii RtcA in complex with a 3′-phosphate terminated RNA and adenosine in the AMP-binding pocket. Our data reveal that RtcA recognizes substrate RNA by ensuring that the terminal 3′-phosphate makes a large contribution to RNA binding. Furthermore, ε the RNA 3′-phosphate is poised for in-line attack on the P–N bond that links the phosphorous atom of AMP to N of His307. Thus, we provide the first insights into RNA 3′-phosphate termini recognition and the mechanism of 3′-phosphate activation by an Rtc enzyme. Keywords: RtcA; RNA 3′-phosphate termini; 2′,3′-cyclic phosphate termini INTRODUCTION AMP (Filipowicz et al. 1985; Billy et al. 1999; Tanaka et al. ′ ′ 2010). RNA 2 ,3 -cyclic phosphate termini play a significant role in RtcA and the tRNA ligase RtcB are the only known enzymes RNA metabolism as intermediates in the chemical- or en- ′ that activate RNA 3 -p termini (Filipowicz et al.
    [Show full text]
  • Affinity Isolation and Mass Spectrometry
    biology Article Affinity Isolation and Mass Spectrometry Identification of Prostacyclin Synthase (PTGIS) Subinteractome Pavel V. Ershov 1,*, Yuri V. Mezentsev 1, Arthur T. Kopylov 1 , Evgeniy O. Yablokov 1 , Andrey V. Svirid 2, Aliaksandr Ya. Lushchyk 2, Leonid A. Kaluzhskiy 1 , Andrei A. Gilep 2, Sergey A. Usanov 2, Alexey E. Medvedev 1 and Alexis S. Ivanov 1 1 Department of Proteomic Research and Mass Spectrometry, Institute of Biomedical Chemistry (IBMC), 10 Pogodinskaya str., 119121 Moscow, Russia; [email protected] (Y.V.M.); [email protected] (A.T.K.); [email protected] (E.O.Y.); [email protected] (L.A.K.); [email protected] (A.E.M.); [email protected] (A.S.I.) 2 Laboratory of Molecular Diagnostics and Biotechnology, Institute of Bioorganic Chemistry of the National Academy of Sciences of Belarus, 5, bld. 2 V.F. Kuprevich str., 220141 Minsk, Belarus; [email protected] (A.V.S.); [email protected] (A.Y.L.); [email protected] (A.A.G.); [email protected] (S.A.U.) * Correspondence: [email protected] Received: 24 April 2019; Accepted: 18 June 2019; Published: 20 June 2019 Abstract: Prostacyclin synthase (PTGIS; EC 5.3.99.4) catalyzes isomerization of prostaglandin H2 to prostacyclin, a potent vasodilator and inhibitor of platelet aggregation. At present, limited data exist on functional coupling and possible ways of regulating PTGIS due to insufficient information about protein–protein interactions in which this crucial enzyme is involved. The aim of this study is to isolate protein partners for PTGIS from rat tissue lysates. Using CNBr-activated Sepharose 4B with covalently immobilized PTGIS as an affinity sorbent, we confidently identified 58 unique proteins by mass spectrometry (LC-MS/MS).
    [Show full text]
  • Escherichia Coli Genome-Wide Promoter Analysis
    Pilalis et al. BMC Genomics 2011, 12:238 http://www.biomedcentral.com/1471-2164/12/238 RESEARCHARTICLE Open Access Escherichia coli genome-wide promoter analysis: Identification of additional AtoC binding target elements Eleftherios Pilalis1, Aristotelis A Chatziioannou1*, Asterios I Grigoroudis2, Christos A Panagiotidis3, Fragiskos N Kolisis1 and Dimitrios A Kyriakidis1,2* Abstract Background: Studies on bacterial signal transduction systems have revealed complex networks of functional interactions, where the response regulators play a pivotal role. The AtoSC system of E. coli activates the expression of atoDAEB operon genes, and the subsequent catabolism of short-chain fatty acids, upon acetoacetate induction. Transcriptome and phenotypic analyses suggested that atoSC is also involved in several other cellular activities, although we have recently reported a palindromic repeat within the atoDAEB promoter as the single, cis-regulatory binding site of the AtoC response regulator. In this work, we used a computational approach to explore the presence of yet unidentified AtoC binding sites within other parts of the E. coli genome. Results: Through the implementation of a computational de novo motif detection workflow, a set of candidate motifs was generated, representing putative AtoC binding targets within the E. coli genome. In order to assess the biological relevance of the motifs and to select for experimental validation of those sequences related robustly with distinct cellular functions, we implemented a novel approach that applies Gene Ontology Term Analysis to the motif hits and selected those that were qualified through this procedure. The computational results were validated using Chromatin Immunoprecipitation assays to assess the in vivo binding of AtoC to the predicted sites.
    [Show full text]
  • Protein Aggregation Patterns Inform About Breast Cancer Response to Antiestrogens and Reveal the RNA Ligase RTCB As Mediator of Acquired Tamoxifen Resistance
    cancers Article Protein Aggregation Patterns Inform about Breast Cancer Response to Antiestrogens and Reveal the RNA Ligase RTCB as Mediator of Acquired Tamoxifen Resistance Inês Direito 1, Liliana Monteiro 1 ,Tânia Melo 2, Daniela Figueira 1, João Lobo 3,4,5 , Vera Enes 1, Gabriela Moura 1, Rui Henrique 3,4,5 , Manuel A. S. Santos 1, Carmen Jerónimo 3,4,5 , Francisco Amado 2, Margarida Fardilha 1 and Luisa A. Helguero 1,* 1 iBiMED—Institute of Biomedicine, University of Aveiro, 3810-193 Aveiro, Portugal; [email protected] (I.D.); [email protected] (L.M.); dfdfi[email protected] (D.F.); [email protected] (V.E.); [email protected] (G.M.); [email protected] (M.A.S.S.); [email protected] (M.F.) 2 LaQV-REQUIMTE—Associated Laboratory for Green Chemistry of the Network of Chemistry and Technology, University of Aveiro, 3810-193 Aveiro, Portugal; [email protected] (T.M.); [email protected] (F.A.) 3 Department of Pathology, Portuguese Oncology Institute of Porto (IPOP), 4200-072 Porto, Portugal; [email protected] (J.L.); [email protected] (R.H.); [email protected] (C.J.) 4 Cancer Biology and Epigenetics Group, IPO Porto Research Center (GEBC CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto) & Porto Comprehensive Cancer Center (P.CCC), 4200-072 Porto, Portugal 5 Department of Pathology and Molecular Immunology, Institute of Biomedical Sciences Abel Salazar, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal * Correspondence: [email protected] Citation: Direito, I.; Monteiro, L.; Melo, T.; Figueira, D.; Lobo, J.; Enes, V.; Moura, G.; Henrique, R.; Santos, Simple Summary: Acquired resistance to antiestrogenic therapy remains the major obstacle to curing M.A.S.; Jerónimo, C.; et al.
    [Show full text]