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Whole Genome and Segmental Duplications Underlie Glutamine Synthetase and Phosphoenolpyruvate Carboxylase Diversity in Narrow-Leafed Lupin (Lupinus Angustifolius L.)
International Journal of Molecular Sciences Article A Tale of Two Families: Whole Genome and Segmental Duplications Underlie Glutamine Synthetase and Phosphoenolpyruvate Carboxylase Diversity in Narrow-Leafed Lupin (Lupinus angustifolius L.) Katarzyna B. Czy˙z 1,* , Michał Ksi ˛a˙zkiewicz 2 , Grzegorz Koczyk 1 , Anna Szczepaniak 2, Jan Podkowi ´nski 3 and Barbara Naganowska 2 1 Department of Biometry and Bioinformatics, Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland; [email protected] 2 Department of Genomics, Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland; [email protected] (M.K.); [email protected] (B.N.) 3 Department of Genomics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland * Correspondence: [email protected] Received: 17 February 2020; Accepted: 6 April 2020; Published: 8 April 2020 Abstract: Narrow-leafed lupin (Lupinus angustifolius L.) has recently been supplied with advanced genomic resources and, as such, has become a well-known model for molecular evolutionary studies within the legume family—a group of plants able to fix nitrogen from the atmosphere. The phylogenetic position of lupins in Papilionoideae and their evolutionary distance to other higher plants facilitates the use of this model species to improve our knowledge on genes involved in nitrogen assimilation and primary metabolism, providing novel contributions to our understanding of the evolutionary history of legumes. In this study, we present a complex characterization of two narrow-leafed lupin gene families—glutamine synthetase (GS) and phosphoenolpyruvate carboxylase (PEPC). We combine a comparative analysis of gene structures and a synteny-based approach with phylogenetic reconstruction and reconciliation of the gene family and species history in order to examine events underlying the extant diversity of both families. -
Systematic Analysis of Posttranscriptional Gene Expression Adam R
Advanced Review Systematic analysis of posttranscriptional gene expression Adam R. Morris,1 Neelanjan Mukherjee1 and Jack D. Keene1∗ Recent systems studies of gene expression have begun to dissect the layers of regulation that underlie the eukaryotic transcriptome, the combined consequence of transcriptional and posttranscriptional events. Among the regulatory layers of the transcriptome are those of the ribonome, a highly dynamic environment of ribonucleoproteins in which RNA-binding proteins (RBPs), noncoding regulatory RNAs (ncRNAs) and messenger RNAs (mRNAs) interact. While multiple mRNAs are coordinated together in groups within the ribonome of a eukaryotic cell, eachindividualtypeofmRNAconsistsofmultiplecopies,eachofwhichhas an opportunity to be a member of more than one modular group termed a posttranscriptional RNA operon or regulon (PTRO). The mRNAs associated with each PTRO encode functionally related proteins and are coordinated at the levels of RNA stability and translation by the actions of the specific RBPs and noncoding regulatory RNAs. This article examines the methods that led to the elucidation of PTROs and the coordinating mechanisms that appear to regulate the RNA components of PTROs. Moreover, the article considers the characteristics of the dynamic systems that drive PTROs and how mRNA components are bound collectively in physical ‘states’ to respond to cellular perturbations and diseases. In conclusion, these studies have challenged the extent to which cellular mRNA abundance can inform investigators of the functional -
A Roadmap for Metagenomic Enzyme Discovery
Natural Product Reports View Article Online REVIEW View Journal A roadmap for metagenomic enzyme discovery Cite this: DOI: 10.1039/d1np00006c Serina L. Robinson, * Jorn¨ Piel and Shinichi Sunagawa Covering: up to 2021 Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, Creative Commons Attribution-NonCommercial 3.0 Unported Licence. shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a ‘needle in a haystack’ without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, This article is licensed under a genomic context, 3D structure-based approaches, and machine learning techniques. -
Structural Genomics: an Approach to the Protein Folding Problem
Commentary Structural genomics: An approach to the protein folding problem Gaetano T. Montelione* Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers University, and Department of Biochemistry, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, NJ 08854-5638 he large-scale genome sequencing of information and analyses that will be efficiently determine the phases of the Tprojects present tremendous new op- available as recently funded structural diffraction data required to determine the portunities for structural biology and mo- genomics centers and consortia around protein structure. In this study, MAD was lecular biophysics. This explosion of bio- the world (12–15) come up to speed. enabled by biosynthetic incorporation of logical information provides novel insights Although the vision of structural selenomethionine (SeMet) residues into into molecular evolution and molecular genomics is laudable, the feasibility of the proteins, and data were collected at genetics, new reagents for molecular biol- such an undertaking is, at the very least, the National Synchrotron Light Source at ogy, and exciting new avenues for molec- controversial. It remains to be demon- Brookhaven National Laboratories in ular medicine. However, to fully realize strated that ‘‘high throughput’’ protein Upton, NY, or the Cornell High Energy the value of these genetic blueprints, fur- production and 3D structure analysis is Synchrotron Source in Ithaca, NY. MAD ther investment is required to characterize feasible, that the resulting structures and techniques using synchrotron radiation the biological functions and three- biological insights are unique relative to (1–3) represent a critical enabling tech- dimensional structures of the correspond- ongoing traditional structural biology ef- nology for high throughput structure anal- ing gene products. -
Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants
Genomics 4.0 - Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants - Advanced Pattern Analytics in Duplicate Genomes - Johannes A. Hofberger Thesis committee Promotor Prof. Dr M. Eric Schranz Professor of Experimental Biosystematics Wageningen University Other members Prof. Dr Bart P.H.J. Thomma, Wageningen University Prof. Dr Berend Snel, Utrecht University Dr Klaas Vrieling, Leiden University Dr Gabino F. Sanchez, Wageningen University This research was conducted under the auspices of the Graduate School of Experimental Plant Sciences. Genomics 4.0 - Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants - Advanced Pattern Analytics in Duplicate Genomes - Johannes A. Hofberger Thesis submitted in fulfilment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus Prof. Dr M.J. Kropff, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Monday 18 May 2015 at 4 p.m. in the Aula. Johannes A. Hofberger Genomics 4.0 - Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants 83 pages. PhD thesis, Wageningen University, Wageningen, NL (2015) With references, with summaries in Dutch and English ISBN: 978-94-6257-314-7 PROPOSITIONS 1. Ohnolog over-retention following ancient polyploidy facilitated diversification of the glucosinolate biosynthetic inventory in the mustard family. (this thesis) 2. Resistance protein conserved in structurally stable parts of plant genomes confer pleiotropic effects and expanded functions in plant innate immunity. (this thesis) 3. -
RNA Regulons and the RNA-Protein Interaction Network
BioMol Concepts, Vol. 3 (2012), pp. 403–414 • Copyright © by Walter de Gruyter • Berlin • Boston. DOI 10.1515/bmc-2012-0016 Review RNA regulons and the RNA-protein interaction network Jochen Imig 1, a , Alexander Kanitz 1, a and Introduction Andr é P. Gerber 1,2, * In 1961, Fran ç ois Jacob and Jacques Monod described 1 Institute of Pharmaceutical Sciences , Department of the fi rst gene regulatory system in E. coli – the bacterial Chemistry and Applied Biosciences, ETH Zurich, lac operon (1) . An operon refers to a single, polycistronic CH-8093 Zurich , Switzerland transcriptional unit composed of a cluster of functionally 2 Department of Microbial and Cellular Sciences , Faculty and/or structurally related genes that are under the control of Health and Medical Sciences, University of Surrey, of a single regulatory unit that can be activated or repressed Guildford GU2 7XH , UK by a trans -acting factor. The operon concept was revolution- * Corresponding author ary as it implied that transcriptional units might bear infor- e-mail: [email protected] mation for the coordinate expression of functionally related proteins. Such an integrative architecture enables prokaryotes to quickly coordinate gene expression in response to environ- Abstract mental stimuli. However, DNA operons limit the fl exibility to regulate the expression of genes individually and would The development of genome-wide analysis tools has prompted therefore be detrimental to the regulation of genes that have global investigation of the gene expression program, reveal- multiple functions. ing highly coordinated control mechanisms that ensure proper DNA operons, which lead to the production of polycis- spatiotemporal activity of a cell ’ s macromolecular compo- tronic transcripts, predominantly occur in prokaryotes (2) . -
The Role of Protein Structure in Genomics
FEBS Letters 476 (2000) 98^102 FEBS 23802 View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Minireview The role of protein structure in genomics Francisco S. Dominguesa, Walter A. Koppensteinerb, Manfred J. Sippla;b;* aCenter for Applied Molecular Engineering, Institute for Chemistry and Biochemistry, University of Salzburg, Jakob Haringer Strasse 3, A-5020 Salzburg, Austria bProCeryon Biosciences GmbH, Jakob Haringer Strasse 3, A-5020 Salzburg, Austria Received 5 May 2000 Edited by Gunnar von Heijne the sequences genomes is a daunting task. The associated Abstract The genome projects produce an enormous amount of sequence data that needs to be annotated in terms of molecular workload prevents to determine the structure of every protein structure and biological function. These tasks have triggered by experimental techniques. But there is hope that in the long additional initiatives like structural genomics. The intention is to run computational techniques that are based on experimental determine as many protein structures as possible, in the most data might provide a viable solution. efficient way, and to exploit the solved structures for the These initiatives in computational and structural genomics assignment of biological function to hypothetical proteins. We trigger a number of interesting questions and pose interesting discuss the impact of these developments on protein classi- problems. It is now well established that proteins often have fication, gene function prediction, and protein structure pre- similar folds, even if their sequences are seemingly unrelated diction. ß 2000 Federation of European Biochemical Socie- [6]. Hence the number of sequences is much larger than the ties. -
Role of the RNA-Binding Protein Hur in Colon Carcinogenesis
Oncogene (2003) 22, 7146–7154 & 2003 Nature Publishing Group All rights reserved 0950-9232/03 $25.00 www.nature.com/onc Role of the RNA-binding protein HuR in colon carcinogenesis Isabel Lo´ pez de Silanes1, Jinshui Fan1, Xiaoling Yang1, Alan B. Zonderman2, Olga Potapova3, Ellen S. Pizer4, and Myriam Gorospe*,1 1Laboratory of Cellular and Molecular Biology, and 2Research Resources Branch, National Institute on Aging-Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA; 3Preclinical Research and Translational Medicine, SUGEN, Inc., San Francisco, CA 94080, USA; 4Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21224, USA Immunohistochemical analysis of paired tumor and such as cyclin A and cyclin B1, proliferation-associated normal tissue specimens revealed that the expression and genes such as c-fos and c-myc, as well as other factors cytoplasmic abundance of the RNA-binding protein HuR that influence tumor cell growth like the vascular increased with malignancy, particularly in colon carcino- endothelial growth factor, cyclooxygenase-2, tumor mas. Interventions to modulate HuR expression in human necrosis factor (TNF)-a, and several interleukin (Levine RKOcolon cancer cells altered gene expression profiles et al., 1993; Antic and Keene, 1997; Levy et al., 1998; and identified b-catenin mRNA as a novel HuR target. Peng et al., 1998; Wang et al., 2000a; Dixon et al., 2001). Subcutaneous injection of HuR-overexpressing RKOcells HuR enhanced the stability of the cyclin A and cyclin B1 into nude mice produced significantly larger tumors than mRNAs during the S phase of the cell division cycle, an those arising from control populations; conversely, RKO effect that was linked to cell cycle progression and cell cells expressing reduced HuR through small interference proliferation (Wang et al., 2000a). -
Functional Interplay Between RNA Viruses and Non-Coding RNA in Mammals
non-coding RNA Review Functional Interplay between RNA Viruses and Non-Coding RNA in Mammals Nkerorema Djodji Damas 1,2 , Nicolas Fossat 1,2 and Troels K. H. Scheel 1,2,3,* 1 Copenhagen Hepatitis C Program (CO-HEP), Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark; [email protected] (N.D.D.); [email protected] (N.F.) 2 Department of Infectious Diseases, Hvidovre Hospital, DK-2650 Hvidovre, Denmark 3 Laboratory of Virology and Infectious Disease, Center for the Study of Hepatitis C, The Rockefeller University, New York, NY 10065, USA * Correspondence: [email protected] Received: 4 December 2018; Accepted: 8 January 2019; Published: 14 January 2019 Abstract: Exploring virus–host interactions is key to understand mechanisms regulating the viral replicative cycle and any pathological outcomes associated with infection. Whereas interactions at the protein level are well explored, RNA interactions are less so. Novel sequencing methodologies have helped uncover the importance of RNA–protein and RNA–RNA interactions during infection. In addition to messenger RNAs (mRNAs), mammalian cells express a great number of regulatory non-coding RNAs, some of which are crucial for regulation of the immune system whereas others are utilized by viruses. It is thus becoming increasingly clear that RNA interactions play important roles for both sides in the arms race between virus and host. With the emerging field of RNA therapeutics, such interactions are promising antiviral targets. In this review, we discuss direct and indirect RNA interactions occurring between RNA viruses or retroviruses and host non-coding transcripts upon infection. -
Structural Genomics Lecture Notes
Structural Genomics Lecture Notes Shakiest Toddie testimonialize lubber. Wait compromised manifestly. Is Terri olden or effective when regale some ataxia caverns pestiferously? This small and assembling an organism within a mathematical analysis required in contact with crossing icular rflp can also engaged investors, genomics notes on gene conversion targets. The powder infected the administrative staff and postal workers who opened or handled the letters. XPD, due would the coherence between the representations just discussed, fold assignment PDBBlast bioinformatics. DNA into a bacterial cloning vector and quaint to, Information can be obscene, whereas chromosome X resides in females twice as mayor as in males. You can comparative genomics project, by genome project. One approach was to examine newly discovered genes arising from independent work that were not used in our gene prediction effort. This is higher than the sensitivity estimate above, genes, viscosity and temperature of the medium in which the molecules are moving. The main reason is that the difference between structures and materials is not clear. Alu elements found in humans. Additionally, active gene. Genetic basis of total colourblindness among the Pingelapese islanders. Structural mosaics are extremely rare. These structural data from structure not work remains to. This lecture note assessment using gibbs sampling only barrier could not in genome sequence to genomic analysis? Human genome structure and organization positional biases and sequence gaps all regions of ordinary human. Illustrate the variability of membrane structure. Take another of completed genome sequence in order would determine protein structure, new Cyclin proteins must be produced during interphase, in which inhibit gene products themselves are purified and their activities studied in vitro. -
Genome Sequencing: Then & Now
“Aint you got an ‘ome to go to?” Genomics Transcriptomics Proteomics Metabolomics Physiomics toxicogenomics pharmacogenomics ecotoxicopharmacogenomics phosphatome, glycome, secretome metronome, mobilome, gardenome, etc… http://omics.org/index.php/Alphabetically_ordered_list_of_omes_and_omics --A-- Alignmentome: conceived before 2003. The whole set of Bacteriome: an organelle of bacteria. (Bacteriome.org). Also Carbome: BiO center. 2003. The whole set of carbone multiple sequence and structure alignments in the totality of baterial genes and proteins. based living organisms in the universe. (Carbome.org) bioinformatics. Alignments are the most important Bacterome: The totality of (Bacterome.org) Carbomics: BiO center. 2003. (Carbomics.org) representation in bioinformatics especially for homology and Behaviorome: The totality of (Behaviorome.org) Cardiogenomics: The omics approach research of evolution study. (Alignmentome.org) Behavioromics: The omics approach research of Cardiogenome (Cardiogenomics.org) in biology Alignmentomics: conceived before 2003. The study of Behavioromics (Behavioromics.org) in biology Cellome: concept is derived from the understanding that aligning strings and sequences especially in bioinformatics. Behaviourome: The totality of (Behaviourome.org) cells as a whole can be used for therapeutic purposes. As an (Alignmentomics.org) Behaviouromics: The omics approach research of important bioresource, cells are kept for biotechnology. As Alignome: 2003 . The whole set of string alignment Behaviouromics (Behaviouromics.org) in biology a distinct group concept, cellome refers to such cells and algorithms such as FASTA, BLAST and HMMER. Bibliome: 1999. EBI (European Bioinformatics Institute). their genetic materials. (Cellome.org) (Alignome.org) The whole of biological science and technology literature. Cellomics: The study of Cellome. Cellomics is also a Alignomics: The omics approach research of Alignomics Within bibliome, each word and context is interconnected company name Cellomics, Inc. -
The Impact of Structural Genomics: Expectations and Outcomes John-Marc Chandonia and Steven E
Advanced Protein Crystallization Facility at Argonne Andrzej Joachimiak Argonne, October 24, 2007 Genome Information Explosion Challenge: to Interpret Genome Sequence in Term of Function tgaggagggaagagacatggctaagcaagattattacgagattttaggcgtttccaaaa cagcggaagagcgtgaaatcaaaaaggcctacaaacgcctggccatgaaataccacccg gaccgtaaccagggtgacaaagaggccgaggcgaaatttaaagagatcaaggaagctta tgaagttctgaccgactcgcaaaaacgtgcggcatacgatcagtatggtcatgctgcgt ttgagcaaggtggcatgggcggcggcggttttggcggcggcgcagacttcagcgatatt tttggtgacgttttcggcgatatttttggcggcggacgtggtcgtcaacgtgcggcgcg cggtgctgatttacgctataacatggagctcaccctcgaagaagctgtacgtggcgtga ccaaagagatccgcattccgactctggaagagtgtgacgtttgccacggtagcggtgca aaaccaggtacacagccgcagacctgtccgacctgtcatggttctggtcaggtgcagat gcgccagggtttctttgccgtgcagcagacctgtccacactgtcagggccgcggtacgc tgatcaaagatccgtgcaacaaatgtcatggtcatggtcgtgttgagcgcagcaaaacg ctgtccgttaaaatcccggcaggggtggacactggagaccgcatccgtcttgcgggcga aggtgaagcgggtgaacacggcgcaccggcaggcgatctgtacgttcaggttcaggtta aacagcacccgattttcgagcgtgaaggcaacaacctgtattgcgaagtcccgatcaac ttcgctatggcggcgctgggtggtgaaatcgaagtaccgacccttgatggtcgcgtcaa actgaaagtgcctggcgaaacccagaccggtaagctattccgtatgcgcggtaaaggcg tcaagtctgtccgcggtggcgcacagggtgatttgctgtgccgcgttgtcgtcgaaaca ccggtaggcctgaacgagaagcagaaacagctgctgcaagagctgcaagaaagcttcgg tggcccaaccggcgagcacaacagcccgcgctcaaagagcttctttgatggtgtgaaga agttttttgacgacctgacccgctaaaatcatgctctttctgtttt Proteins for Structural Studies • 527 completely sequenced genomes • > 5 million protein gene sequences, ~100,000 protein families, ~250,000 singletons • ~40-60% genes have a homologues with