Genome Evolution: Mutation Is the Main Driver of Genome Size in Prokaryotes Gabriel A.B
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Specificity of Genome Evolution in Experimental Populations Of
Specificity of genome evolution in experimental PNAS PLUS populations of Escherichia coli evolved at different temperatures Daniel E. Deatheragea,b,c,d, Jamie L. Kepnera,b,c,d, Albert F. Bennette, Richard E. Lenskif,g,1, and Jeffrey E. Barricka,b,c,d,f,1 aCenter for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712; bCenter for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712; cInstitute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712; dDepartment of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712; eDepartment of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697; fBEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824; and gDepartment of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824 Edited by Neil H. Shubin, The University of Chicago, Chicago, IL, and approved January 25, 2017 (received for review September 27, 2016) Isolated populations derived from a common ancestor are expected to produce only in environments within a restricted thermal range. diverge genetically and phenotypically as they adapt to different local Adaptation for improved thermotolerance has important impli- environments. To examine this process, 30 populations of Escherichia cations for plant growth (5) and crop productivity (6), especially coli were evolved for 2,000 generations, with six in each of five dif- in light of climate change (7). Directed evolution of microor- ferent thermal regimes: constant 20 °C, 32 °C, 37 °C, 42 °C, and daily ganisms, such as yeast, to function effectively at higher or lower alternations between 32 °C and 42 °C. -
Genome Analysis of the Smallest Free-Living Eukaryote Ostreococcus
Genome analysis of the smallest free-living eukaryote SEE COMMENTARY Ostreococcus tauri unveils many unique features Evelyne Derellea,b, Conchita Ferrazb,c, Stephane Rombautsb,d, Pierre Rouze´ b,e, Alexandra Z. Wordenf, Steven Robbensd, Fre´ de´ ric Partenskyg, Sven Degroeved,h, Sophie Echeynie´ c, Richard Cookei, Yvan Saeysd, Jan Wuytsd, Kamel Jabbarij, Chris Bowlerk, Olivier Panaudi, BenoıˆtPie´ gui, Steven G. Ballk, Jean-Philippe Ralk, Franc¸ois-Yves Bougeta, Gwenael Piganeaua, Bernard De Baetsh, Andre´ Picarda,l, Michel Delsenyi, Jacques Demaillec, Yves Van de Peerd,m, and Herve´ Moreaua,m aObservatoire Oce´anologique, Laboratoire Arago, Unite´Mixte de Recherche 7628, Centre National de la Recherche Scientifique–Universite´Pierre et Marie Curie-Paris 6, BP44, 66651 Banyuls sur Mer Cedex, France; cInstitut de Ge´ne´ tique Humaine, Unite´Propre de Recherche 1142, Centre National de la Recherche Scientifique, 141 Rue de Cardonille, 34396 Montpellier Cedex 5, France; dDepartment of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology and eLaboratoire Associe´de l’Institut National de la Recherche Agronomique (France), Ghent University, Technologiepark 927, 9052 Ghent, Belgium; fRosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149; gStation Biologique, Unite´Mixte de Recherche 7144, Centre National de la Recherche Scientifique–Universite´Pierre et Marie Curie-Paris 6, BP74, 29682 Roscoff Cedex, France; hDepartment of Applied Mathematics, Biometrics and -
Comparative Genome Evolution Working Group
Comparative Genome Evolution Working Group COMPARATIVE GENOME EVOLUTION MODIFIED PROPOSAL Introduction Analysis of comparative genomic sequence information from a well-chosen set of organisms is, at present, one of the most effective approaches available to advance biomedical research. The following document describes rationales and plans for selecting targets for genome sequencing that will provide insight into a number of major biological questions that broadly underlie major areas of research funded by the National Institutes of Health, including studies of gene regulation, understanding animal development, and understanding gene and protein function. Genomic sequence data is a fundamental information resource that is required to address important questions about biology: What is the genomic basis for the advent of major morphogenetic or physiological innovations during evolution? How have genomes changed with the addition of new features observed in the eukaryotic lineage, for example the development of an adaptive immune system, an organized nervous system, bilateral symmetry, or multicellularity? What are the genomic correlates or bases for major evolutionary phenomena such as evolutionary rates; speciation; genome reorganization, and origins of variation? Another vital use to which genomic sequence data are applied is the development of more robust information about important non-human model systems used in biomedical research, i.e. how can we identify conserved functional regions in the existing genome sequences of important non- mammalian model systems, so that we can better understand fundamental aspects of, for example, gene regulation, replication, or interactions between genes? NHGRI established a working group to provide the Institute with well-considered scientific thought about the genomic sequences that would most effectively address these questions. -
Section 4. Guidance Document on Horizontal Gene Transfer Between Bacteria
306 - PART 2. DOCUMENTS ON MICRO-ORGANISMS Section 4. Guidance document on horizontal gene transfer between bacteria 1. Introduction Horizontal gene transfer (HGT) 1 refers to the stable transfer of genetic material from one organism to another without reproduction. The significance of horizontal gene transfer was first recognised when evidence was found for ‘infectious heredity’ of multiple antibiotic resistance to pathogens (Watanabe, 1963). The assumed importance of HGT has changed several times (Doolittle et al., 2003) but there is general agreement now that HGT is a major, if not the dominant, force in bacterial evolution. Massive gene exchanges in completely sequenced genomes were discovered by deviant composition, anomalous phylogenetic distribution, great similarity of genes from distantly related species, and incongruent phylogenetic trees (Ochman et al., 2000; Koonin et al., 2001; Jain et al., 2002; Doolittle et al., 2003; Kurland et al., 2003; Philippe and Douady, 2003). There is also much evidence now for HGT by mobile genetic elements (MGEs) being an ongoing process that plays a primary role in the ecological adaptation of prokaryotes. Well documented is the example of the dissemination of antibiotic resistance genes by HGT that allowed bacterial populations to rapidly adapt to a strong selective pressure by agronomically and medically used antibiotics (Tschäpe, 1994; Witte, 1998; Mazel and Davies, 1999). MGEs shape bacterial genomes, promote intra-species variability and distribute genes between distantly related bacterial genera. Horizontal gene transfer (HGT) between bacteria is driven by three major processes: transformation (the uptake of free DNA), transduction (gene transfer mediated by bacteriophages) and conjugation (gene transfer by means of plasmids or conjugative and integrated elements). -
Evolution of Genome Size
Evolution of Genome Advanced article Article Contents Size • Introduction • How Much Variation Is There? Stephen I Wright, Department of Ecology and Evolutionary Biology, University • What Types of DNA Drive Genome Size of Toronto, Toronto, Ontario, Canada Variation? • Neutral Model • Nearly Neutral Model • Adaptive Hypotheses • Transposable Element Evolution • Conclusion • Acknowledgements Online posting date: 16th January 2017 The size of the genome represents one of the most in the last century. While considerable progress has been made strikingly variable yet poorly understood traits in the characterisation of the extent of genome size variation, in eukaryotic organisms. Genomic comparisons the dominant evolutionary processes driving genome size evolu- suggest that most properties of genomes tend tion remain subject to considerable debate. Large-scale genome sequencing is enabling new insights into both the proximate to increase with genome size, but the fraction causes and evolutionary forces governing genome size differ- of the genome that comprises transposable ele- ences. ments (TEs) and other repetitive elements tends to increase disproportionately. Neutral, nearly neutral and adaptive models for the evolution of How Much Variation Is There? genome size have been proposed, but strong evi- dence for the general importance of any of these Because determining the amount of DNA (deoxyribonucleic models remains lacking, and improved under- acid) in a cell has been much more straightforward and cheaper standing of factors driving the -
Best Practices for Whole Genome Sequencing Using the Sequel System
Best Practices for Whole Genome Sequencing Using the Sequel System Justin Blethrow, Nick Sisneros, Shreyasee Chakraborty, Sarah Kingan, Richard Hall, Joan Wilson, Christine Lambert, Kevin Eng, Emily Hatas and Primo Baybayan PacBio, 1305 O’Brien Dr., Menlo Park, CA 94025 Library Construction Abstract Recommendations Data Analysis Plant and animal whole genome sequencing has proven to Recommended Shearing Devices for Large-insert Fragments Hierarchical Genome Assembly Process (HGAP) and Polishing be challenging, particularly due to genome size, high For shearing DNA, PacBio recommends either: 1) needle shearing with a 26 G needle, which density of repetitive elements and heterozygosity. The allows for flexibility in number of shearing pulses with the needle or 2) the Megaruptor, a simple, Sequel System delivers long reads, high consensus automated, and highly reproducible system to fragment DNA up to 75 kb. accuracy and uniform coverage, enabling more complete, accurate, and contiguous assemblies of these large complex genomes. The latest Sequel chemistry increases yield up to 8 Gb per SMRT Cell for long insert libraries >20 kb and up to 10 Gb per SMRT Cell for libraries >40 kb. In addition, the recently released SMRTbell Express Megaruptor® DNA Shearing System Template Prep Kit reduces the time (~3 hours) and DNA Demonstration of Needle Shearing input (~3 µg), making the workflow easy to use for multi- SMRT Cell projects. 1 2 3 4 5 Here, we recommend the best practices for whole genome HGAP1 utilizes all PacBio data using the longest reads for contiguity and all reads to sequencing and de novo assembly of complex plant and generate high-quality de novo assemblies with high consensus accuracy (>QV50). -
Genomes and Their Evolution
CAMPBELL BIOLOGY IN FOCUS URRY • CAIN • WASSERMAN • MINORSKY • REECE 18 Genomes and Their Evolution Lecture Presentations by Kathleen Fitzpatrick and Nicole Tunbridge, Simon Fraser University © 2016 Pearson Education, Inc. SECOND EDITION Overview: Reading Leaves from the Tree of Life . Complete genome sequences exist for a human, chimpanzee, E. coli, and numerous other prokaryotes, as well as corn, fruit fly, house mouse, orangutan, and others . Comparisons of genomes among organisms provide information about the evolutionary history of genes and taxonomic groups © 2016 Pearson Education, Inc. Genomics is the study of whole sets of genes and their interactions . Bioinformatics is the application of computational methods to the storage and analysis of biological data © 2016 Pearson Education, Inc. Figure 18.1 © 2016 Pearson Education, Inc. Concept 18.1: The Human Genome Project fostered development of faster, less expensive sequencing techniques . The Human Genome Project officially began in 1990, and the sequencing was largely completed by 2003 . Even with automation, the sequencing of all 3 billion base pairs in a haploid set presented a formidable challenge . A major thrust of the Human Genome Project was the development of technology for faster sequencing © 2016 Pearson Education, Inc. The whole-genome shotgun approach was developed by J. Craig Venter and colleagues . This approach starts with cloning and sequencing random DNA fragments . Powerful computer programs are used to assemble the resulting short overlapping sequences into a single continuous sequence © 2016 Pearson Education, Inc. Figure 18.2-s1 Cut the DNA into overlapping fragments short enough for sequencing. Clone the fragments in plasmid or other vectors. © 2016 Pearson Education, Inc. -
Chapter 5 Genome Sequences and Evolution
© Jones & Bartlett Learning LLC, an Ascend Learning Company. NOT FOR SALE OR DISTRIBUTION. CHAPTER 5 © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION Genome Sequences © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC and ENOTvolu FOR SALEtion OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION CHAPTER OUTLINE © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC 5.1 Introduction 5.13 A Constant Rate of Sequence Divergence Is NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION 5.2 Prokaryotic Gene Numbers Range Over an a Molecular Clock Order of Magnitude 5.14 The Rate of Neutral Substitution Can Be 5.3 Total Gene Number Is Known for Several Measured from Divergence of Repeated Eukaryotes Sequences © Jones &5.4 Bartlett How Many Learning, Different LLC Types of Genes Are © Jones5.15 &How Bartlett Did Interrupted Learning, Genes LLC Evolve? NOT FOR SALEThere? OR DISTRIBUTION NOT 5.16FOR Why SALE Are OR Some DISTRIBUTION Genomes So Large? 5.5 The Human Genome Has Fewer Genes Than 5.17 Morphological Complexity Evolves by Originally Expected Adding New Gene Functions 5.6 How -
Decoding Non-Coding DNA: Trash Or Treasure?
GENERAL ARTICLE Decoding Non-Coding DNA: Trash or Treasure? Namrata Iyer Non-coding DNA, once thought of as ‘junk’, represents a very large portion of an organism’s genome. However, recent research has brought to light many functional elements present within non-coding DNA sequences and unravelled a fascinat- ing array of functions performed by these elements. These findings have highlighted the nature of the evolutionary forces that led to the accumulation and retention of non-coding Namrata Iyer is a PhD DNA. In this article, the various elements present within non- student in the Department of Microbiology and Cell coding DNA, their functional relevance to the cell and the Biology, Indian Institute of changing perspective of the scientific community towards this Science, Bangalore. Her so-called ‘junk’ DNA have been described. research interest is the molecular basis of host– Since the dawn of time, man has always been plagued by the pathogen interactions in question of the origin of life. What are the forces that govern the human diseases. course of evolution? What are the elements that separate man from other forms of life? The discovery of DNA (deoxyribo- nucleic acid) as the genetic material in 1944 opened up new avenues to answer these questions. Surprisingly, the language of DNA comprises of only 4 letters, i.e., A,T,G,C which when read in groups of three (triplets) encode the information for the synthe- sis of proteins (by a process known as translation) which are the work-horses of a cell. Before translation can begin, the informa- tion on DNA is first copied into an intermediate known as mRNA (by a process known as transcription). -
On the Law of Directionality of Genome Evolution
On the Law of Directionality of Genome Evolution Liaofu Luo Laboratory of Theoretical Biophysics, Faculty of Science and Technology, Inner Mongolia University, Hohhot 010021, China *Email address: [email protected] Abstract The problem of the directionality of genome evolution is studied from the information-theoretic view. We propose that the function-coding information quantity of a genome always grows in the course of evolution through sequence duplication, expansion of code, and gene transfer between genomes. The function-coding information quantity of a genome consists of two parts, p-coding information quantity which encodes functional protein and n-coding information quantity which encodes other functional elements except amino acid sequence. The relation of the proposed law to the thermodynamic laws is indicated. The evolutionary trends of DNA sequences revealed by bioinformatics are investigated which afford further evidences on the evolutionary law. It is argued that the directionality of genome evolution comes from species competition adaptive to environment. An expression on the evolutionary rate of genome is proposed that the rate is a function of Darwin temperature (describing species competition) and fitness slope (describing adaptive landscape). Finally, the problem of directly experimental test on the evolutionary directionality is discussed briefly. 1 A law of genomic information The traditional natural sciences mostly focused on matter and energy. Of course, the two are basic categories in the nature. However, parallel to yet different from matter and energy, information constitutes the third fundamental category in natural sciences. One characteristic of a life system is that it contains a large amount of information. Life consists of matter and energy, but it is not just matter and energy. -
The Neoselectionist Theory of Genome Evolution
The neoselectionist theory of genome evolution Giorgio Bernardi* Molecular Evolution Laboratory, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy Edited by Tomoko Ohta, National Institute of Genetics, Mishima, Japan, and approved April 4, 2007 (received for review February 22, 2007) The vertebrate genome is a mosaic of GC-poor and GC-rich isoch- of chance in evolution. A very significant modification of the ores, megabase-sized DNA regions of fairly homogeneous base neutral theory was the nearly neutral theory of Ohta (11, 12), composition that differ in relative amount, gene density, gene who proposed that a substantial fraction of changes are caused expression, replication timing, and recombination frequency. At by random fixation of nearly neutral changes, a class that the emergence of warm-blooded vertebrates, the gene-rich, mod- ‘‘includes intermediates between neutral and advantageous, as erately GC-rich isochores of the cold-blooded ancestors underwent well as between neutral and deleterious classes’’ (13). The first a GC increase. This increase was similar in mammals and birds and four schemes of Fig. 1 display a qualitative picture of the classical was maintained during the evolution of mammalian and avian theories just mentioned. orders. Neither the GC increase nor its conservation can be ac- All classical theories proposed that natural selection acted on counted for by the random fixation of neutral or nearly neutral the ‘‘phenotype’’ [called the ‘‘classical phenotype’’ by Bernardi single-nucleotide changes (i.e., the vast majority of nucleotide and Bernardi (14), to distinguish it from the ‘‘genome pheno- substitutions) or by a biased gene conversion process occurring at type’’ presented below]. -
How Much Non-Coding DNA?
ARTICLE IN PRESS Journal of Theoretical Biology 252 (2008) 587–592 www.elsevier.com/locate/yjtbi How much non-coding DNA do eukaryotes require? Sebastian E. Ahnerta,c,Ã, Thomas M.A. Finkb, Andrei Zinovyeva aInstitut Curie, Bioinformatics, 26 rue d’Ulm, Paris 75248, France bInstitut Curie, CNRS UMR 144, 26 rue d’Ulm, Paris 75248, France cTheory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK Received 13 May 2007; received in revised form 5 February 2008; accepted 5 February 2008 Available online 14 February 2008 Abstract Despite tremendous advances in the field of genomics, the amount and function of the large non-coding part of the genome in higher organisms remains poorly understood. Here we report an observation, made for 37 fully sequenced eukaryotic genomes, which indicates that eukaryotes require a certain minimum amount of non-coding DNA (ncDNA). This minimum increases quadratically with the amount of DNA located in exons. Based on a simple model of the growth of regulatory networks, we derive a theoretical prediction of the required quantity of ncDNA and find it to be in excellent agreement with the data. The amount of additional ncDNA (in basepairs) which eukaryotes require obeys N 1/2 (N /N ) (N N ), where N is the amount of exonic DNA, and N is a constant of about DEF ¼ C P CÀ P C P 10 Mb. This value NDEF corresponds to a few percent of the genome in Homo sapiens and other mammals, and up to half the genome in simpler eukaryotes. Thus, our findings confirm that eukaryotic life depends on a substantial fraction of ncDNA and also make a prediction of the size of this fraction, which matches the data closely.