Genetic Variability Description in a Wide Germplasm of Domesticated
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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. -
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. -
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. -
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. -
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 -
Classical Genetics 3. the Beginnings of Genomic Biol
Table of Contents: Preface 3.3.2. Eukaryotic chromosome structure Websites of Interest 3.3.3. Heterochromatin & Euchromatin 3.4. DNA Replication Glossary 3.4.1. DNA replication is semiconservative 1. Introduction 3.4.2. DNA polymerases 1.1. What is a Gene? 3.4.3. Initiation of replication 1.2. What is a Genome? 3.4.4. DNA replication is semidiscontinuous 1.3. What is Genomic Biology? 3.4.5. DNA replication in Eukaryotes. 1.3.1. Structural Genomics 3.4.6. Replicating ends of chromosomes 1.3.2. Comparative Genomics 3.5. Transcription 1.3.3. Functional Genomics 3.5.1. Cellular RNAs are transcribed from DNA 1.4. Genomic Databases 3.5.2. RNA polymerases catalyze transcription 3.5.3. Transcription in Prokaryotes 2. The beginnings of Genomic Biology – classical 3.5.4. Transcription in Prokaryotes - Polycistronic mRNAs genetics are produced from operons 2.1. Mendel & Darwin – traits are conditioned by genes 3.5.5. Beyond Operons – Modification of expression in 2.2. Genes are carried on chromosomes Prokaryotes 2.3. The chromosomal theory of inheritance 3.5.6. Transcriptions in Eukaryotes 2.4. Additional Complexity of Mendelian Inheritance 3.5.7. Processing primary transcripts into mature mRNA 2.4.1. Multiple alleles 3.6. Translation 2.4.2. Incomplete dominance and co-dominance 3.6.1. The Nature of Proteins 2.4.3. Sex linked inheritance 2.4.4. Epistasis 3.6.2. The Genetic Code 2.4.5. Epigenetics 3.6.3. tRNA – The decoding molecule 2.5. The Law of Independent Assortment 3.6.4. -
Interrogating Protein Interaction Networks Through Structural Biology
Interrogating protein interaction networks through structural biology Patrick Aloy and Robert B. Russell* European Molecular Biology Laboratory (EMBL), Postfach 10 22 09, D-69012, Heidelberg, Germany Communicated by I. M. Gelfand, Rutgers, The State University of New Jersey-New Brunswick, Highland Park, NJ, March 13, 2002 (received for review November 16, 2001) Protein–protein interactions are central to most biological processes. databank suggests considerable variation in the interaction partners Although much recent effort has been put into methods to identify preferred by particular protein families (14). Clearly detailed stud- interacting partners, there has been a limited focus on how these ies are required to understand when it is possible to infer an interactions compare with those known from three-dimensional (3D) interaction between proteins when one is known to occur between structures. Because comparison of protein interactions often involves homologues. considering homologous, but not identical, proteins, a key issue is Here, we present a method to model putative interactions on whether proteins that are homologous to an interacting pair will known 3D complexes and to assess the compatibility of a proposed interact in the same way, or interact at all. Accordingly, we describe protein–protein interaction with such a complex. After identifying a method to test putative interactions on complexes of known 3D the residues that make atomic contacts in a known crystallographic structure. Given a 3D complex and alignments of homologues of the complex, we look to homologues of both interacting proteins to see interacting proteins, we assess the fit of any possible interacting pair whether these interactions are preserved by means of empirical on the complex by using empirical potentials. -
Unexpected Features of the Dark Proteome
Unexpected features of the dark proteome Nelson Perdigãoa,b, Julian Heinrichc, Christian Stoltec, Kenneth S. Sabird,e, Michael J. Buckleyc, Bruce Taborc, Beth Signald, Brian S. Glossd, Christopher J. Hammangd, Burkhard Rostf, Andrea Schafferhansf, and Seán I. O’Donoghuec,d,g,1 aInstituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; bInstituto de Sistemas e Robótica, 1049-001 Lisbon, Portugal; cCommonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW 1670, Australia; dGenomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; eSchool of Information Technology, The University of Sydney, Sydney, NSW 2006, Australia; fDepartment for Bioinformatics and Computational Biology, Technische Universität München, 80333 Munich, Germany; and gSchool of Molecular Bioscience, The University of Sydney, Sydney, NSW 2006, Australia Edited by Alan R. Fersht, Medical Research Council Laboratory of Molecular Biology, Cambridge, United Kingdom, and approved October 13, 2015 (received for review April 29, 2015) We surveyed the “dark” proteome–that is, regions of proteins never In fact, discoveries have already resulted from studying regions observed by experimental structure determination and inaccessible of unknown structure, namely, intrinsically disordered regions. to homology modeling. For 546,000 Swiss-Prot proteins, we found Long known to confound structure determination (15)—thus that 44–54% of the proteome in eukaryotes and viruses was dark, forming part of the dark proteome—disorder was largely ignored ∼ compared with only 14% in archaea and bacteria. Surprisingly, until recently (16) and yet is now known to play key functional most of the dark proteome could not be accounted for by conven- roles, especially in eukaryotes (17). -
National Science Foundation-Sponsored Workshop Report
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln U.S. Department of Agriculture: Agricultural Publications from USDA-ARS / UNL Faculty Research Service, Lincoln, Nebraska 5-2004 National Science Foundation-Sponsored Workshop Report. Draft Plan for Soybean Genomics Gary Stacey University of Missouri, Columbia, [email protected] Lila n Vodkin University of Illinois, Urbana Wayne A. Parrott The University of Georgia, Athens, Georgia Randy C. Shoemaker Iowa State University Follow this and additional works at: https://digitalcommons.unl.edu/usdaarsfacpub Part of the Agricultural Science Commons Stacey, Gary; Vodkin, Lila n; Parrott, Wayne A.; and Shoemaker, Randy C., "National Science Foundation- Sponsored Workshop Report. Draft Plan for Soybean Genomics" (2004). Publications from USDA-ARS / UNL Faculty. 297. https://digitalcommons.unl.edu/usdaarsfacpub/297 This Article is brought to you for free and open access by the U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Publications from USDA-ARS / UNL Faculty by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Meeting Report National Science Foundation-Sponsored Workshop Report. Draft Plan for Soybean Genomics1 Gary Stacey*, Lila Vodkin, Wayne A. Parrott, and Randy C. Shoemaker National Center for Soybean Biotechnology, Department of Plant Microbiology and Pathology, University of Missouri, Columbia, Missouri -
Structural Genomics of the Thermotoga Maritima Proteome Implemented in a High-Throughput Structure Determination Pipeline
Structural genomics of the Thermotoga maritima proteome implemented in a high-throughput structure determination pipeline Scott A. Lesley*†, Peter Kuhn‡, Adam Godzik§, Ashley M. Deacon‡, Irimpan Mathews‡, Andreas Kreusch*, Glen Spraggon*, Heath E. Klock*, Daniel McMullan*, Tanya Shin*, Juli Vincent*, Alyssa Robb*, Linda S. Brinen‡, Mitchell D. Miller‡, Timothy M. McPhillips‡, Mark A. Miller§, Daniel Scheibe*¶, Jaume M. Canaves§, Chittibabu Guda§, Lukasz Jaroszewski§, Thomas L. Selbyʈ, Marc-Andre Elsligerʈ, John Wooley§**, Susan S. Taylor¶, Keith O. Hodgson‡, Ian A. Wilsonʈ, Peter G. Schultz*ʈ, and Raymond C. Stevensʈ *Joint Center for Structural Genomics, Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121; ‡Joint Center for Structural Genomics, Stanford Synchrotron Radiation Laboratory, Stanford University, 2575 Sand Hill Road, MS99, Menlo Park, CA 94025; Joint Center for Structural Genomics, §San Diego Supercomputer Center and **University of California, 9500 Gilman Drive, La Jolla, CA 92093; and ʈJoint Center for Structural Genomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 Contributed by Peter G. Schultz, July 11, 2002 Structural genomics is emerging as a principal approach to define An HT pipeline requires integrating technology and process protein structure–function relationships. To apply this approach on development. To this end, a large set of easily accessible genes a genomic scale, novel methods and technologies must be devel- is required. T. maritima is an attractive target for a structural oped to determine large numbers of structures. We describe genomics research program, as its small genome (1,877 genes) the design and implementation of a high-throughput structural makes it practical for isolating the entire recombinant proteome.