Viral Mirna Adaptor Differentially Recruits Mirnas to Target Mrnas Through Alternative Base-Pairing Carlos Gorbea1, Tim Mosbruger2, David a Nix3, Demia´ N Cazalla1*
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Non-Coding Rnas: Strategy for Viruses' Offensive
non-coding RNA Review Non-Coding RNAs: Strategy for Viruses’ Offensive Alessia Gallo 1,*, Matteo Bulati 1, Vitale Miceli 1 , Nicola Amodio 2 and Pier Giulio Conaldi 1,3 1 Department of Research, IRCCS ISMETT (Istituto Mediterraneo per i Trapianti e Terapie ad alta specializzazione), Via E.Tricomi 5, 90127 Palermo, Italy; [email protected] (M.B.); [email protected] (V.M.); [email protected] (P.G.C.) 2 Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; [email protected] 3 UPMC Italy (University of Pittsburgh Medical Center Italy), Discesa dei Giudici 4, 90133 Palermo, Italy * Correspondence: [email protected]; Tel.: +39-91-21-92-649 Received: 7 August 2020; Accepted: 8 September 2020; Published: 10 September 2020 Abstract: The awareness of viruses as a constant threat for human public health is a matter of fact and in this resides the need of understanding the mechanisms they use to trick the host. Viral non-coding RNAs are gaining much value and interest for the potential impact played in host gene regulation, acting as fine tuners of host cellular defense mechanisms. The implicit importance of v-ncRNAs resides first in the limited genomes size of viruses carrying only strictly necessary genomic sequences. The other crucial and appealing characteristic of v-ncRNAs is the non-immunogenicity, making them the perfect expedient to be used in the never-ending virus-host war. In this review, we wish to examine how DNA and RNA viruses have evolved a common strategy and which the crucial host pathways are targeted through v-ncRNAs in order to grant and facilitate their life cycle. -
Degeneracy and Genetic Assimilation in RNA Evolution Reza Rezazadegan1* and Christian Reidys1,2
Rezazadegan and Reidys BMC Bioinformatics (2018) 19:543 https://doi.org/10.1186/s12859-018-2497-3 RESEARCH ARTICLE Open Access Degeneracy and genetic assimilation in RNA evolution Reza Rezazadegan1* and Christian Reidys1,2 Abstract Background: The neutral theory of Motoo Kimura stipulates that evolution is mostly driven by neutral mutations. However adaptive pressure eventually leads to changes in phenotype that involve non-neutral mutations. The relation between neutrality and adaptation has been studied in the context of RNA before and here we further study transitional mutations in the context of degenerate (plastic) RNA sequences and genetic assimilation. We propose quasineutral mutations, i.e. mutations which preserve an element of the phenotype set, as minimal mutations and study their properties. We also propose a general probabilistic interpretation of genetic assimilation and specialize it to the Boltzmann ensemble of RNA sequences. Results: We show that degenerate sequences i.e. sequences with more than one structure at the MFE level have the highest evolvability among all sequences and are central to evolutionary innovation. Degenerate sequences also tend to cluster together in the sequence space. The selective pressure in an evolutionary simulation causes the population to move towards regions with more degenerate sequences, i.e. regions at the intersection of different neutral networks, and this causes the number of such sequences to increase well beyond the average percentage of degenerate sequences in the sequence space. We also observe that evolution by quasineutral mutations tends to conserve the number of base pairs in structures and thereby maintains structural integrity even in the presence of pressure to the contrary. -
Folding RNA/DNA Hybrid Duplexes
Vol. 00 no. 00 BIOINFORMATICS Pages 1–2 Folding RNA/DNA hybrid duplexes Ronny Lorenz 1,∗, Ivo L Hofacker 1,2 and Stephan H Bernhart 2,∗ 1Institute for Theoretical Chemistry, University Vienna, Wahringer¨ Straße 17, 1090 Vienna, Austria 2Research group BCB, Faculty of Computer Science, University Vienna 3Department of Bioinformatics, University of Leipzig, Haertelstraße 16-18, 04109 Leipzig, Germany Received on XXXXX; revised on XXXXX; accepted on XXXXX Associate Editor: XXXXXXX ABSTRACT (for a review see e.g. [11]) or even their dimers, there is, to our Motivation: While there are numerous programs that can predict knowledge, no program that also includes the possibility to predict RNA or DNA secondary structures, a program that predicts RNA/DNA the full secondary structure of RNA/DNA hetero-dimers. This may hetero-dimers is still missing. The lack of easy to use tools for be due to the lack of compiled energy parameters, as there are only predicting their structure may be in part responsible for the small stacking energies available today. We introduce here a possibility number of reports of biologically relevant RNA/DNA hetero-dimers. to predict the secondary structure of such hetero-dimers within the Results: We present here an extension to the widely used widely used ViennaRNA Package. ViennaRNA Package [6] for the prediction of the structure of RNA/DNA hetero-dimers. Availability: http://www.tbi.univie.ac.at/˜ronny/RNA/vrna2.html 2 APPROACH Contact: [email protected] 2.1 Adaptation of the ViennaRNA Package For the prediction of RNA/DNA hetero-dimers, three distinct energy 1 INTRODUCTION parameter sets (RNA, DNA and mixed) are necessary. -
Supplemental Material Predicting Transfer RNA Gene Activity From
Supplemental Material Predicting transfer RNA gene activity from sequence and genome context 1 1,2 1 1 Bryan P. Thornlow , Joel Armstrong , Andrew D. Holmes , Jonathan M. Howard , Russell B. 1,2 1,2 Corbett-Detig , Todd M. Lowe 1 2 D epartment of Biomolecular Engineering, University of California, Santa Cruz, CA 95064; G enomics Institute, University of California, Santa Cruz, CA 95064 Supplemental Material Supplemental Text Supplemental Methods Supplemental Figure S1 Supplemental Figure S2 Supplemental Figure S3 Supplemental Figure S4 Supplemental Figure S5 Supplemental Figure S6 Supplemental Figure S7 Supplemental Figure S8 Supplemental Figure S9 Supplemental Figure S10 Supplemental Figure S11 Supplemental References SUPPLEMENTAL TEXT: Possible tRNA gene classification and assembly errors. Ideally, we would observe that all species contained at least one tRNA gene per expected anticodon (excluding anticodons for which no active tRNA gene is observed in human or mouse). We found three exceptions to this rule. C. hircus (goat) is the only species without a predicted active tRNA-Ser-TGA gene. C. hircus is also one of only three species that has any tRNA-Ser-GGA genes, and it has 27 such genes, one of which was predicted active. These 27 tRNA-Ser-GGA genes do exhibit some sequence similarity, as 21 of these share a 10-base 3’ flanking motif (AGAAGGAAAT). They may therefore reflect a very recent proliferation event. However, it is more likely that they represent assembly errors, because we have previously shown that the immediate 3’ flanking regions of tRNA genes experience high mutation rates (Thornlow et al. 2018), these 27 tRNA genes are found on 15 different chromosomes, and Ser-GGA is not a standard eukaryotic tRNA (Grosjean et al. -
Ribotoolkit: an Integrated Platform for Analysis and Annotation of Ribosome Profiling Data to Decode Mrna Translation at Codon R
W218–W229 Nucleic Acids Research, 2020, Vol. 48, Web Server issue Published online 19 May 2020 doi: 10.1093/nar/gkaa395 RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution Qi Liu1,2,*, Tanya Shvarts3, Piotr Sliz2,3 and Richard I. Gregory 1,2,4,5,6,* 1Stem Cell Program, Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA, 2Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA, 3Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA 02115, USA, 4Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA, 5Harvard Initiative for RNA Medicine, Boston, MA 02115, USA and 6Harvard Stem Cell Institute, Cambridge, MA 02138, USA Received March 06, 2020; Revised April 23, 2020; Editorial Decision May 02, 2020; Accepted May 15, 2020 ABSTRACT INTRODUCTION Ribosome profiling (Ribo-seq) is a powerful technol- Ribosome profiling (Ribo-seq), also known as ribosome ogy for globally monitoring RNA translation; ranging footprinting, has revolutionized the ‘translatomics’ field from codon occupancy profiling, identification of ac- by mapping the position of ribosome-protected fragments tively translated open reading frames (ORFs), to the (RPFs), which typically range in length from 26 to 34 nu- quantification of translational efficiency under vari- cleotides (nt), over the entire transcriptome (1,2). The sci- entific community has employed Ribo-seq to answer awide ous physiological or experimental conditions. How- range of questions, ranging from the identification of trans- ever, analyzing and decoding translation information lated open reading frames (ORFs) to the quantification from Ribo-seq data is not trivial. -
Comparative Ribosome Profiling Uncovers a Dominant Role for Translational Control in Toxoplasma Gondii
Comparative ribosome profiling uncovers a dominant role for translational control in Toxoplasma gondii The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hassan, Musa A. et al. "Comparative ribosome profiling uncovers a dominant role for translational control in Toxoplasma gondii." BMC Genomics 18 (December 2017): 961 © 2017 The Author(s) As Published http://dx.doi.org/10.1186/s12864-017-4362-6 Publisher BioMed Central Version Final published version Citable link http://hdl.handle.net/1721.1/114431 Terms of Use Creative Commons Attribution Detailed Terms http://creativecommons.org/licenses/by/4.0/ Hassan et al. BMC Genomics (2017) 18:961 DOI 10.1186/s12864-017-4362-6 RESEARCH ARTICLE Open Access Comparative ribosome profiling uncovers a dominant role for translational control in Toxoplasma gondii Musa A. Hassan1,2*, Juan J. Vasquez3,8, Chew Guo-Liang4, Markus Meissner5,6 and T. Nicolai Siegel3,6,7 Abstract Background: The lytic cycle of the protozoan parasite Toxoplasma gondii, which involves a brief sojourn in the extracellular space, is characterized by defined transcriptional profiles. For an obligate intracellular parasite that is shielded from the cytosolic host immune factors by a parasitophorous vacuole, the brief entry into the extracellular space is likely to exert enormous stress. Due to its role in cellular stress response, we hypothesize that translational control plays an important role in regulating gene expression in Toxoplasma during the lytic cycle. Unlike transcriptional profiles, insights into genome-wide translational profiles of Toxoplasma gondii are lacking. Methods: We have performed genome-wide ribosome profiling, coupled with high throughput RNA sequencing, in intracellular and extracellular Toxoplasma gondii parasites to investigate translational control during the lytic cycle. -
Assessing the Quality of Cotranscriptional Folding Simulations
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.06.895607; this version posted January 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Assessing the Quality of Cotranscriptional Folding Simulations Felix Kühnl, Peter F. Stadler, and Sven Findeiß Abstract Structural changes in RNAs are an important contributor to controlling gene expression not only at the post-transcriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5’ region of the primary transcript regulates the downstream functional unit – usually an ORF – through premature termination of transcription. Such elements not only occur naturally but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labour-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach both because of the size of the molecules and the time scales of interest. Even at the simplified level of secondary structures further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape F. -
Control of RNA Function by Conformational Design
DISSERTATION / DOCTORAL THESIS Titel der Dissertation /Title of the Doctoral Thesis Control of RNA function by conformational design verfasst von / submitted by Mag. rer. nat. Stefan Badelt angestrebter akademischer Grad / in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) Wien, 2016 / Vienna, 2016 Studienkennzahl lt. Studienblatt / A 794 685 490 degree programme code as it appears on the student record sheet: Dissertationsgebiet lt. Studienblatt / Molekulare Biologie field of study as it appears on the student record sheet: Betreut von / Supervisor: Univ.-Prof. Dipl.-Phys. Dr. Ivo Hofacker Design landscapes >Output Input AGGAACAGUCGCACU ACCCACCUCGACAUC AGCAACA GUAAAUCAAAUUGGA AGGAUCU ACUGAAGCCCUUGGU AGGAUCA CUGGAGUCACCAGGG objective function AGGAACA GGUUUACGUACUACU design Energy landscapes 0.7 0.7 >Input -8.0 Output 3.9 3.7 1.1 2.8 1.2 1.3 2.3 0.8 AGGAACAGUCGCACU 3.5 2.6 2.6 2.8 3.9 2.3 -10.0 3.0 4.2 1.2 0.7 2.3 1.2 4.4 1.3 0.599999 1.3 2.4 1.0 0.8 100 1.5 2.0 1.2 0.8 97 98 99 2.8 1.4 1.0 94 95 93 96 92 2.3 0.7 2.6 0.8 91 1.5 1.3 2.0 2.6 1.6 89 90 1.9 0.8 2.0 86 88 87 2.9 2.3 1.9 1.3 1.3 ACCCACCUCGACAUC 0.6 83 85 84 82 2.6 1.0 0.8 0.8 77 81 79 78 80 0.7 76 1.9 74 75 73 1.5 72 0.7 -12.0 0.8 0.8 70 71 1.1 0.799999 1.2 0.8 69 68 0.8 0.8 0.8 63 65 64 66 67 0.9 1.2 1.2 59 60 58 61 62 1.2 1.4 1.7 1.2 55 54 57 56 3.1 3.0 4.2 52 53 51 49 50 0.7 1.0 45 44 46 47 48 42 43 41 3.5 1.0 38 39 40 1.3 0.6 2.6 1.3 0.8 0.8 0.8 GUAAAUCAAAUUGGA 37 35 36 34 32 33 31 30 28 29 27 1.2 0.8 24 25 26 23 -14.0 22 20 21 2.4 17 -
Emerging Roles for Natural Microrna Sponges
Emerging Roles for Natural MicroRNA Sponges The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Ebert, Margaret S., and Phillip A. Sharp. “Emerging Roles for Natural MicroRNA Sponges.” Current Biology 20, no. 19 (October 2010): R858–R861. © 2010 Elsevier. As Published http://dx.doi.org/10.1016/j.cub.2010.08.052 Publisher Elsevier B.V. Version Final published version Citable link http://hdl.handle.net/1721.1/96080 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Current Biology 20, R858–R861, October 12, 2010 ª2010 Elsevier Ltd All rights reserved DOI 10.1016/j.cub.2010.08.052 Emerging Roles for Natural MicroRNA Minireview Sponges Margaret S. Ebert and Phillip A. Sharp miRNAs in a variety of systems: in vitro differentiation of neurons [3] and mesenchymal stem cells [4]; xenografts of cancer cell lines [5,6]; and bone marrow reconstitutions Recently, a non-coding RNA expressed from a human from hematopoietic stem/progenitor cells [7–9]. Germline pseudogene was reported to regulate the corresponding transgenic fruitflies have been shown to generate hypomor- protein-coding mRNA by acting as a decoy for microRNAs phic miRNA phenotypes when sponge expression is induced (miRNAs) that bind to common sites in the 30 untranslated in a tissue-specific manner via the Gal4–UAS system [10]. regions (UTRs). It was proposed that competing for miRNAs might be a general activity of pseudogenes. -
Machine Learning a Model for RNA Structure Prediction
Machine learning a model for RNA structure prediction Nicola Calonaci1, Alisha Jones2,3, Francesca Cuturello1, Michael Sattler2,3, and Giovanni Bussi1 1International School for Advanced Studies, via Bonomea 265, 34136 Trieste, Italy 2Institute of Structural Biology, Helmoltz Zentrum M¨unchen, Neuherberg 85764, Germany 3Center for Integrated Protein Science M¨unchen and Bavarian NMR Center at Department of Chemistry, Technical University of Munich, Garching 85757, Germany Abstract RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on com- puting the partition function of folding ensembles, and can thus esti- mate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless comple- mented by auxiliary experimental data. Here, we build a set of mod- els that combine thermodynamic parameters, chemical probing data (DMS, SHAPE), and co-evolutionary data (Direct Coupling Analysis, DCA) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble popu- lations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window com- bines neighboring reactivities, enlightening their structural information arXiv:2004.00351v2 [q-bio.BM] 5 Oct 2020 content and the contribution of local conformational ensembles. Regu- larization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensem- ble populations for native structures and more accurate predictions in an independent validation set. -
A Tale of Two Rnas During Viral Infection: How Viruses Antagonize Mrnas and Small Non-Coding Rnas in the Host Cell
viruses Review A Tale of Two RNAs during Viral Infection: How Viruses Antagonize mRNAs and Small Non-Coding RNAs in The Host Cell Kristina M. Herbert 1,* and Anita Nag 2,* 1 Department of Experimental Microbiology, Center for Scientific Research and Higher Education of Ensenada (CICESE), Ensenada, Baja California 22860, Mexico 2 Department of Chemistry, Florida A&M University, Tallahassee, FL 32307, USA * Correspondence: [email protected] (K.M.H.); [email protected] (A.N.); Tel.: +52-646-175-0500 (K.M.H.); +1-850-399-5658 (A.N.) Academic Editor: Alexander Ploss Received: 29 March 2016; Accepted: 20 May 2016; Published: 2 June 2016 Abstract: Viral infection initiates an array of changes in host gene expression. Many viruses dampen host protein expression and attempt to evade the host anti-viral defense machinery. Host gene expression is suppressed at several stages of host messenger RNA (mRNA) formation including selective degradation of translationally competent messenger RNAs. Besides mRNAs, host cells also express a variety of noncoding RNAs, including small RNAs, that may also be subject to inhibition upon viral infection. In this review we focused on different ways viruses antagonize coding and noncoding RNAs in the host cell to its advantage. Keywords: mRNA; endonuclease; host shut-off; virus; RNAi 1. Introduction Viral infection has significant effects on the host’s gene expression program. Upon viral infection, mammalian cells activate specific gene expression pathways as a defense response. To assure survival, viruses manipulate the host’s gene expression response pattern in order to maximize viral replication and/or to evade detection by, and block the, host immune responses. -
Murine Cytomegalovirus Encodes a Mir-27 Inhibitor Disguised As a Target
Murine cytomegalovirus encodes a miR-27 inhibitor disguised as a target Valentina Libria,1, Aleksandra Helwakb,1, Pascal Miesenc, Diwakar Santhakumara,d, Jessica G. Borger a, Grzegorz Kudlab, Finn Greye, David Tollerveyb, and Amy H. Bucka,d,2 aCentre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom; bWellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3JR, United Kingdom; cDepartment of Medical Microbiology, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, 6500 HB, Nijmegen, The Netherlands; dDivision of Pathway Medicine, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom; and eThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, United Kingdom Edited* by Norman R. Pace, University of Colorado, Boulder, CO, and approved November 29, 2011 (received for review September 1, 2011) Individual microRNAs (miRNAs) are rapidly down-regulated during involved (16). A small nuclear RNA (snRNA) in Herpesvirus conditions of cellular activation and infection, but factors mediat- saimiri (HVS), HSUR-1, was recently shown to bind to miR-27 ing miRNA turnover are poorly understood. Infection of mouse and mediate its degradation (17). However, no homolog of cells with murine cytomegalovirus (MCMV) induces the rapid HSUR-1 has been identified in other viral families. miRNA down-regulation of an antiviral cellular miRNA, miR-27. Here, we turnover mechanisms in animals remain poorly characterized identify a transcript produced by MCMV that binds to miR-27 and (18). The aim of this work was to identify miR-27 interaction sites mediates its degradation.