Gram Negative Bacteria Have Evolved a Series of Secretion Systems Which

Total Page:16

File Type:pdf, Size:1020Kb

Gram Negative Bacteria Have Evolved a Series of Secretion Systems Which A Bioinformatics Analysis of Bacterial Type-III Secretion System Genes and Proteins By Christopher Michael Bailey A thesis submitted to The University of Birmingham for the degree of DOCTOR OF PHILOSOPHY Division of Immunity and Infection The School of Medicine The University of Birmingham November 2010 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract Type-III secretion systems (T3SSs) are responsible for the biosynthesis of flagella, and the interaction of many animal and plant pathogens with eukaryotic cells. T3SSs consist of multiple proteins which assemble to form an apparatus capable of exporting proteins through both membranes of Gram-negative bacteria in one step. Proteins conserved amongst T3SSS can be used for analysis of these systems using computational homology searching. By using tools including BLAST and HMMER in conjunction phylogenetic analysis this thesis examines the range of T3SSs, both in terms of the proteins they contain, and also the bacteria which contain them. In silico analysis of several of the conserved components of T3SSs shows similarities between them and other secretion systems, as well as components of ATPases. Use of conserved components allows for identification of T3SS loci in diverse bacteria, in order to assess in the different proteins used by different T3SSs, and to see where, in evolutionary space, these differences arose. Analysis of homology data also allows for comprehensive re-annotation of T3SS loci within Desulfovibrio, Lawsonia and Hahella, and subsequent comparison of these T3SSs with related Yersinial T3SSs, and also (in conjunction with in vitro assays) for identification of many novel effectors in E. coli. Authors Contributions All the work presented here is the authors own with the exception of portions of Chapter 2 and Chapter 3. Within Chapter 2 initial survey work leading to the work on the relationship between b-subunit proteins of ATPases and SctL/FliH components was undertaken by Professor Mark Pallen, before being taken on by the Author. Within Chapter 3, the work presented was part of a collaborative effort between three separate lab groups: The Pallen group at the University of Birmingham, The Frankel group at Imperial College, London and the Hayashi group at Osaka University, Japan. Initial bioinformatics survey work was performed by Scott Beatson, all secretome, Flag and CyeA assays were performed by Toru Tobe, Hisaaki Taniguchi and Hiroyuki Abe at Osaka University. -lactamase assays were prepared by the Author, Scott Beatson, Amanda Fivian, Rasha Younis and Sophie Matthews. The -lactamase assay itself was performed under the supervision of Olivier Marches. Out of the complete set of all -lactamase assays the author worked with half of the effector candidates from the stage of production of amplified DNA for effector candidates to production of source and destination gateway plasmids. Of this 50%, the author also and undertook the assay from start to finish for half (i.e. 25% of the all the effector candidates). Dedication To my parents Dorrien and Michael Acknowledgements The division of immunity and infection for funding my work. Prof Mark Pallen for his supervision, ideas, and support. Prof Charles Penn and Dr. Roy Chaudhuri for their stimulating insight. Dr Lori Snyder, Dr Scott Beatson and Dr Nick Loman for proof- reading, and last but not least, Dr. Dov Stekel for his mathematical knowledge, support and advice. Table of Contents Chapter 1 - Introduction ................................................................................................. 1 1.1. Molecular Evolution ............................................................................................... 1 1.1.1. Gene Gain, Gene Loss and Gene Mutation ..................................................... 2 1.1.2. Vertical Gene Transfer..................................................................................... 3 1.1.3. Horizontal Gene Transfer................................................................................. 4 1.1.3.1. Plasmids .................................................................................................... 6 1.1.3.2. Bacteriophage............................................................................................ 6 1.1.4. Homology, Orthology and Parology ................................................................ 8 1.1.5. Detection of evolutionarily related sequences in silico ................................... 9 1.1.5.1. Simple sequence alignment algorithms .................................................. 10 1.1.5.2. Scoring alignments.................................................................................. 14 1.1.5.3. Heuristic methods for searching large datasets: BLAST ........................ 16 1.1.5.4. Detecting distant homology .................................................................... 19 1.1.5.4.1. Alignment based methods: PSI-BLAST .............................................. 20 1.1.5.4.2. Markov model based methods: HMMER ............................................ 21 1.1.6. Sequence similarity as a predictor of structure and function ......................... 24 1.1.6.1. Divergence between sequence, structure and function ........................... 25 1.1.6.2. Conserved sequence, un-conserved structure ......................................... 26 1.1.6.3. Conserved sequence and structure, un-conserved function .................... 27 1.1.6.4. Conserved sequence, unknown function................................................. 27 1.1.6.5. Un-conserved sequence, conserved structure ......................................... 28 1.1.6.6. Un-conserved sequence and structure, conserved function .................... 30 1.1.6.7. Implications for assignments based on sequence similarity alone.......... 31 1.1.7. Phylogenetics and phylogenomics ................................................................. 32 1.1.7.1. Methods for classifying proteins and larger units into trees ................... 33 1.2. Protein Secretion by Bacteria................................................................................ 34 1.2.1. Secretion across the inner membrane............................................................. 35 1.2.2. Secretion across the outer membrane............................................................. 37 1.2.2.1. Type I Secretion ...................................................................................... 37 1.2.2.2. Type II secretion ..................................................................................... 39 1.2.2.3. Type IV secretion.................................................................................... 42 1.2.2.4. Type V secretion ..................................................................................... 43 1.2.2.5. Type III secretion .................................................................................... 45 1.3. Type III Secretion Systems ................................................................................... 46 1.3.1. The bacterial Flagellum and Non-Flagellar secretion systems ...................... 46 1.3.2. The construction of type-III secretion systems .............................................. 47 1.3.2.1. Cytoplasmic and Inner Membrane Proteins............................................ 47 1.3.2.2. Periplasmic and outer membrane proteins .............................................. 51 1.3.3. Directing secretion: T3S needle and translocon ............................................ 53 1.3.3.1. The needle and associated proteins ......................................................... 53 1.3.3.2. The translocon - proteins that put the tip on the needle .......................... 56 1.3.4. Control of apparatus and effectors: Regulators and chaperones.................... 57 1.3.5. Communicating with the host: effector proteins............................................ 60 i 1.4. Viewing Type-III Secretion in an Evolutionary Context...................................... 61 1.5. Sequence – Structure – Function Relationships in Type-III secretion proteins.... 62 1.6. Aims ...................................................................................................................... 63 Chapter 2 - Specific T3ss components......................................................................... 66 2.1. Introduction ........................................................................................................... 66 2.1.1. NF-T3SS components shared with other systems ......................................... 66 2.1.2. The machinery of ATP synthesis and utilisation ........................................... 67 2.1.3. Aims ............................................................................................................... 69 2.2. Methods................................................................................................................. 70 2.2.1. BLAST and PSI-BLAST ..............................................................................
Recommended publications
  • Seq2pathway Vignette
    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
    [Show full text]
  • Extensive Microbial Diversity Within the Chicken Gut Microbiome Revealed by Metagenomics and Culture
    Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture Rachel Gilroy1, Anuradha Ravi1, Maria Getino2, Isabella Pursley2, Daniel L. Horton2, Nabil-Fareed Alikhan1, Dave Baker1, Karim Gharbi3, Neil Hall3,4, Mick Watson5, Evelien M. Adriaenssens1, Ebenezer Foster-Nyarko1, Sheikh Jarju6, Arss Secka7, Martin Antonio6, Aharon Oren8, Roy R. Chaudhuri9, Roberto La Ragione2, Falk Hildebrand1,3 and Mark J. Pallen1,2,4 1 Quadram Institute Bioscience, Norwich, UK 2 School of Veterinary Medicine, University of Surrey, Guildford, UK 3 Earlham Institute, Norwich Research Park, Norwich, UK 4 University of East Anglia, Norwich, UK 5 Roslin Institute, University of Edinburgh, Edinburgh, UK 6 Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Banjul, The Gambia 7 West Africa Livestock Innovation Centre, Banjul, The Gambia 8 Department of Plant and Environmental Sciences, The Alexander Silberman Institute of Life Sciences, Edmond J. Safra Campus, Hebrew University of Jerusalem, Jerusalem, Israel 9 Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, UK ABSTRACT Background: The chicken is the most abundant food animal in the world. However, despite its importance, the chicken gut microbiome remains largely undefined. Here, we exploit culture-independent and culture-dependent approaches to reveal extensive taxonomic diversity within this complex microbial community. Results: We performed metagenomic sequencing of fifty chicken faecal samples from Submitted 4 December 2020 two breeds and analysed these, alongside all (n = 582) relevant publicly available Accepted 22 January 2021 chicken metagenomes, to cluster over 20 million non-redundant genes and to Published 6 April 2021 construct over 5,500 metagenome-assembled bacterial genomes.
    [Show full text]
  • 1985517720.Pdf
    JOURNAL OF BACTERIOLOGY, Jan. 2009, p. 347–354 Vol. 191, No. 1 0021-9193/09/$08.00ϩ0 doi:10.1128/JB.01238-08 Copyright © 2009, American Society for Microbiology. All Rights Reserved. Complete Genome Sequence and Comparative Genome Analysis of Enteropathogenic Escherichia coli O127:H6 Strain E2348/69ᰔ† Atsushi Iguchi,1 Nicholas R. Thomson,2 Yoshitoshi Ogura,1,3 David Saunders,2 Tadasuke Ooka,3 Ian R. Henderson,4 David Harris,2 M. Asadulghani,1 Ken Kurokawa,5 Paul Dean,6 Brendan Kenny,6 Michael A. Quail,2 Scott Thurston,2 Gordon Dougan,2 Tetsuya Hayashi,1,3 Julian Parkhill,2 and Gad Frankel7* Division of Bioenvironmental Science, Frontier Science Research Center,1 and Division of Microbiology, Department of Infectious Diseases, Faculty of Medicine,3 University of Miyazaki, Miyazaki, Japan; Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom2; School of Immunity and Infection, University of Downloaded from Birmingham, Birmingham, United Kingdom4; Department of Biological Information, School and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Kanagawa, Japan5; Institute of Cell and Molecular Biosciences, University of Newcastle, Newcastle upon Tyne, United Kingdom6; and Centre for Molecular Microbiology and Infection, Division of Cell and Molecular Biology, Imperial College London, London, United Kingdom7 Received 5 September 2008/Accepted 15 October 2008 Enteropathogenic Escherichia coli (EPEC) was the first pathovar of E. coli to be implicated in human disease; however, no EPEC strain has been fully sequenced until now. Strain E2348/69 (serotype O127:H6 belonging to E. http://jb.asm.org/ coli phylogroup B2) has been used worldwide as a prototype strain to study EPEC biology, genetics, and virulence.
    [Show full text]
  • Arsenite As an Electron Donor for Anoxygenic Photosynthesis: Description of Three Strains of Ectothiorhodospira from Mono Lake, California and Big Soda Lake, Nevada
    life Article Arsenite as an Electron Donor for Anoxygenic Photosynthesis: Description of Three Strains of Ectothiorhodospira from Mono Lake, California and Big Soda Lake, Nevada Shelley Hoeft McCann 1,*, Alison Boren 2, Jaime Hernandez-Maldonado 2, Brendon Stoneburner 2, Chad W. Saltikov 2, John F. Stolz 3 and Ronald S. Oremland 1,* 1 U.S. Geological Survey, Menlo Park, CA 94025, USA 2 Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, CA 95064, USA; [email protected] (A.B.); [email protected] (J.H.-M.); [email protected] (B.S.); [email protected] (C.W.S.) 3 Department of Biological Sciences, Duquesne University, Pittsburgh, PA 15282, USA; [email protected] * Correspondence: [email protected] (S.H.M.); [email protected] (R.S.O.); Tel.: +1-650-329-4474 (S.H.M.); +1-650-329-4482 (R.S.O.) Academic Editors: Rafael Montalvo-Rodríguez, Aharon Oren and Antonio Ventosa Received: 5 October 2016; Accepted: 21 December 2016; Published: 26 December 2016 Abstract: Three novel strains of photosynthetic bacteria from the family Ectothiorhodospiraceae were isolated from soda lakes of the Great Basin Desert, USA by employing arsenite (As(III)) as the sole electron donor in the enrichment/isolation process. Strain PHS-1 was previously isolated from a hot spring in Mono Lake, while strain MLW-1 was obtained from Mono Lake sediment, and strain BSL-9 was isolated from Big Soda Lake. Strains PHS-1, MLW-1, and BSL-9 were all capable of As(III)-dependent growth via anoxygenic photosynthesis and contained homologs of arxA, but displayed different phenotypes.
    [Show full text]
  • Quadram Institute Newsletter
    Autumn 2019 Welcome to the newsle�er of the Quadram Ins�tute. This issue highlights recent research breakthroughs at the We con�nue to build our team and are pleased to welcome Quadram Ins�tute (QI) that could have posi�ve impacts on Professor Cynthia Whitchurch to the QI. Cynthia is se�ng public health. Working with clinicians our researchers have up a research group inves�ga�ng bacterial lifestyles, and shown how the latest sequencing technologies can aid in how these make them more infec�ous or resistant to diagnos�cs and surveillance. Coupling these techniques an�microbials. Cynthia joins us from the ithree ins�tute at with ‘Big Data’ analy�cal approaches will be vital to the University of Technology Sydney. Her research led to addressing 21st century popula�on health challenges, and the discovery that extracellular DNA is required for biofilm the QI aims to be a leader in this area. development. Big Data in the NHS was the topic of a roundtable We con�nue to welcome visitors to our new building to discussion I a�ended with Patricia Hart at the policy share our vision to understand how food and microbes development think tank Reform. This roundtable was interact to promote health and prevent disease. In June, we sponsored by QI and explored how universi�es and industry had the honour of hos�ng His Excellency Simon Smits, can work with government to realise poten�al applica�ons Ambassador of the Netherlands to the UK. The Ambassador of Big Data. The event was chaired by Baroness Blackwood, visited the Norwich Research Park as part of an Parliamentary Under-Secretary of State, Department of interna�onal trade delega�on to East Anglia to learn about Health and Social Care (DHSC) and included senior the region’s world-leading life sciences research and trade policy-makers, public service prac��oners, academics and opportuni�es.
    [Show full text]
  • Curriculum Vitae Professor Mark John Pallen
    Curriculum Vitae Professor Mark John Pallen MA (Hons) Cantab, MBBS, MD, PhD January 2014 Mark Pallen — Curriculum vitae Personal Details Name Mark John Pallen Date of Birth 6 July 1960 Nationality British Address 17 Lodge Drive, Malvern Worcestershire, WR14 4LS E-mail address [email protected] Telephone 01684 567710 (home); 07824 086946 (mobile) Web Page: http://tinyurl.com/ncul2p3 Twitter: http://twitter.com/mjpallen YouTube Channel: http://www.youtube.com/user/pallenm/ Education and Qualifications PhD 1998 Imperial College, London An investigation into the links between stationary phase and virulence in Salmonella enterica enterica serovar Typhimurium MD 1993 St Bartholomew's Hospital Medical College Detection and characterisation of diphtheria toxin genes and insertion sequences MRCPath by examination in Medical Microbiology 1991 (upgraded to FRCPath 2005) MB BS 1981-84 London Hospital Medical College Undergraduate Prizes: LEPRA National Essay Prize, 1982 Turnbull Prize in Pathology, 1983, 1984 Sutton Prize in Pathology, 1983 BA (Hons) in Medical Sciences 1978-81 University of Cambridge Fitzwilliam College (Lower Second, converted to MA, 1985) Page 1 Mark Pallen — Curriculum vitae Employment Professor of Microbial Genomics Head of Division of Microbiology and Infection Apr 2013-now Warwick Medical School, University of Warwick Professor of Microbial Genomics 2001-2013 University of Birmingham Professor and Head of Department 1999-2001 Department of Microbiology and Immunobiology Queen’s University, Belfast Senior Lecturer (Honorary Consultant) 1992–99 Department of Medical Microbiology St Bartholomew's and the Royal London School of Medicine and Dentistry (Queen Mary Westfield College) Visiting Research Fellow 1994–97 Department of Biochemistry Imperial College of Science, Technology and Medicine (on a Wellcome Trust Research Leave Fellowship, working at Imperial, while still employed by Barts) Lecturer (Hon.
    [Show full text]
  • Revealing the Acute Asthma Ignorome: Characterization and Validation of Uninvestigated Gene Networks
    www.nature.com/scientificreports OPEN Revealing the acute asthma ignorome: characterization and validation of uninvestigated gene Received: 07 December 2015 Accepted: 01 April 2016 networks Published: 21 April 2016 Michela Riba1,*, Jose Manuel Garcia Manteiga1,*, Berislav Bošnjak2,*, Davide Cittaro1, Pavol Mikolka2,†, Connie Le2,‡, Michelle M. Epstein2,# & Elia Stupka1,# Systems biology provides opportunities to fully understand the genes and pathways in disease pathogenesis. We used literature knowledge and unbiased multiple data meta-analysis paradigms to analyze microarray datasets across different mouse strains and acute allergic asthma models. Our combined gene-driven and pathway-driven strategies generated a stringent signature list totaling 933 genes with 41% (440) asthma-annotated genes and 59% (493) ignorome genes, not previously associated with asthma. Within the list, we identified inflammation, circadian rhythm, lung-specific insult response, stem cell proliferation domains, hubs, peripheral genes, and super-connectors that link the biological domains (Il6, Il1ß, Cd4, Cd44, Stat1, Traf6, Rela, Cadm1, Nr3c1, Prkcd, Vwf, Erbb2). In conclusion, this novel bioinformatics approach will be a powerful strategy for clinical and across species data analysis that allows for the validation of experimental models and might lead to the discovery of novel mechanistic insights in asthma. Allergen exposure causes a complex interaction of cellular and molecular networks leading to allergic asthma in susceptible individuals. Experimental mouse models of allergic asthma are widely used to understand disease pathogenesis and elucidate mechanisms underlying the initiation of allergic asthma1. For example, gene profiling of lung tissue from experimental mice during the initiation of allergic asthma in experiments with different proto- cols2–7 validated well-known genes and identified new genes with roles in disease pathogenesis such as C53, Arg18, Adam89, and Pon17, and dissected pathways activated by Il1310 and Stat611.
    [Show full text]
  • Menaquinone As Pool Quinone in a Purple Bacterium
    Menaquinone as pool quinone in a purple bacterium Barbara Schoepp-Cotheneta,1, Cle´ ment Lieutauda, Frauke Baymanna, Andre´ Verme´ gliob, Thorsten Friedrichc, David M. Kramerd, and Wolfgang Nitschkea aLaboratoire de Bioe´nerge´tique et Inge´nierie des Prote´ines, Unite´Propre de Recherche 9036, Institut Fe´de´ ratif de Recherche 88, Centre National de la Recherche Scientifique, F-13402 Marseille Cedex 20, France; bLaboratoire de Bioe´nerge´tique Cellulaire, Unite´Mixte de Recherche 163, Centre National de la Recherche Scientifique–Commissariat a`l’E´ nergie Atomique, Universite´ delaMe´ diterrane´e–Commissariat a`l’E´ nergie Atomique 1000, Commissariat a` l’E´ nergie Atomique Cadarache, Direction des Sciences du Vivant, De´partement d’Ecophysiologie Ve´ge´ tale et Microbiologie, F-13108 Saint Paul Lez Durance Cedex, France; cInstitut fu¨r Organische Chemie und Biochemie, Albert-Ludwigs-Universita¨t Freiburg, Albertstr. 21, D-79104 Freiburg, Germany; and dInstitute of Biological Chemistry, Washington State University, Pullman, WA 99164-6340 Edited by Pierre A. Joliot, Institut de Biologie Physico-Chimique, Paris, France, and approved March 31, 2009 (received for review December 23, 2008) Purple bacteria have thus far been considered to operate light- types of pool-quinones, such as ubi-, plasto-, mena-, rhodo-, driven cyclic electron transfer chains containing ubiquinone (UQ) as caldariella- or sulfolobus-quinones (to cite only the best-studied liposoluble electron and proton carrier. We show that in the purple cases) have been identified so far individually in different species ␥-proteobacterium Halorhodospira halophila, menaquinone-8 or coexisting in single organisms (2–4). (MK-8) is the dominant quinone component and that it operates in Menaquinone (MK) is the most widely distributed quinone on the QB-site of the photosynthetic reaction center (RC).
    [Show full text]
  • Clinical Metagenomics
    Clinical metagenomics Nick Loman Jonathan Eisen Mick Watson 16S vs metagenomics • Cheap • Expensive • Targets single marker • In theory can detect gene anything • Limited to bacteria • Harder to analyse • Relatively easy to analyse • Fewer biases (?) • Lots of known biases • Taxonomic assignment at • Function information species level problematic directly accessible • Function can only be • Strain-level inferred, not detected information • Goes deeper • Shallower Definition of a metagenome • The collection of genomes and genes from the members of a microbiota • Microbiota: The assemblage of microorganisms present in a defined environment. • Microbiome: This term refers to the entire habitat, including the microorganisms, their genomes (i.e., genes) and the surrounding environmental conditions. http://www.allthingsgenomics.com/blog/2013/1/11/the-vocabulary-used-to-describe- microbial-communities-microbiome-metagenome-microbiota Metagenomics – Your questions • What are the best ways to address getting representation of bacteria, viruses, fungi and others? Techniques for doing so? – Thoughts on the use of physical enrichment techniques to isolate microbe of interest rather than traditional metagenomic sequencing? • What are the best bioinformatic software packages and pipelines for functional analysis? – What are the best analysis pipelines for full viral sequencing to detect whether mutations are true or not? Comparing closely related taxa? • As an initial approach, should one try 16s sequencing prior to shotgun sequencing if interested
    [Show full text]
  • Photosynthesis Is Widely Distributed Among Proteobacteria As Demonstrated by the Phylogeny of Puflm Reaction Center Proteins
    fmicb-08-02679 January 20, 2018 Time: 16:46 # 1 ORIGINAL RESEARCH published: 23 January 2018 doi: 10.3389/fmicb.2017.02679 Photosynthesis Is Widely Distributed among Proteobacteria as Demonstrated by the Phylogeny of PufLM Reaction Center Proteins Johannes F. Imhoff1*, Tanja Rahn1, Sven Künzel2 and Sven C. Neulinger3 1 Research Unit Marine Microbiology, GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany, 2 Max Planck Institute for Evolutionary Biology, Plön, Germany, 3 omics2view.consulting GbR, Kiel, Germany Two different photosystems for performing bacteriochlorophyll-mediated photosynthetic energy conversion are employed in different bacterial phyla. Those bacteria employing a photosystem II type of photosynthetic apparatus include the phototrophic purple bacteria (Proteobacteria), Gemmatimonas and Chloroflexus with their photosynthetic relatives. The proteins of the photosynthetic reaction center PufL and PufM are essential components and are common to all bacteria with a type-II photosynthetic apparatus, including the anaerobic as well as the aerobic phototrophic Proteobacteria. Edited by: Therefore, PufL and PufM proteins and their genes are perfect tools to evaluate the Marina G. Kalyuzhanaya, phylogeny of the photosynthetic apparatus and to study the diversity of the bacteria San Diego State University, United States employing this photosystem in nature. Almost complete pufLM gene sequences and Reviewed by: the derived protein sequences from 152 type strains and 45 additional strains of Nikolai Ravin, phototrophic Proteobacteria employing photosystem II were compared. The results Research Center for Biotechnology (RAS), Russia give interesting and comprehensive insights into the phylogeny of the photosynthetic Ivan A. Berg, apparatus and clearly define Chromatiales, Rhodobacterales, Sphingomonadales as Universität Münster, Germany major groups distinct from other Alphaproteobacteria, from Betaproteobacteria and from *Correspondence: Caulobacterales (Brevundimonas subvibrioides).
    [Show full text]
  • SGM Meeting Abstracts
    CONTENTS Page MAIN SYMPOSIUM Microbial subversion of host cells 3 Offered posters 6 GROUP SYMPOSIUM CELLS & CELL SURFACES GROUP Type IV secretion systems 11 Offered posters 12 CLINICAL MICROBIOLOGY GROUP Septicaemia 17 Offered posters 20 CLINICAL MICROBIOLOGY / CLINICAL VIROLOGY / FOOD & BEVERAGES GROUPS Water and environment related infections 25 CLINICAL VIROLOGY GROUP The management of outbreaks 27 Offered posters 28 EDUCATION & TRAINING GROUP Successfully surviving your PhD 31 ENVIRONMENTAL MICROBIOLOGY / SYSTEMATICS & EVOLUTION GROUPS Biological control: mechanisms, function and application 33 Offered posters: Environmental Microbiology Group 36 Systematics & Evolution Group 38 FERMENTATION & BIOPROCESSING & FOOD & BEVERAGES GROUPS / SCOTTISH MICROBIOLOGY SOCIETY Advances in the understanding of microbial contributions to alcoholic beverage fermentations 39 Offered posters: Fermentation & Bioprocessing Group 41 Food & BeveragesGroup 42 MICROBIAL INFECTION GROUP Endothelial cell-pathogen interactions 47 Offered posters 49 PHYSIOLOGY, BIOCHEMISTRY & MOLECULAR GENETICS GROUP / SOCIETY FOR ANAEROBIC MICROBIOLOGY Molecular aspects of anaerobes 55 Offered posters 58 VIRUS GROUP Symposium One - Vaccines 65 Symposuim Two - Viruses & cancer 71 INDEX OF AUTHORS 77 LATE SUBMISSIONS (web version only) 80 VIRUS GROUP – WORKSHOPS - ABSTRACTS (web version only) 82 Society for General Microbiology – 152nd Meeting – University of Edinburgh – 7-11 April 2003 - 1 - Society for General Microbiology – 152nd Meeting – University of Edinburgh – 7-11 April
    [Show full text]
  • Biomolecules
    biomolecules Review Phylogenetic Distribution, Ultrastructure, and Function of Bacterial Flagellar Sheaths Joshua Chu 1, Jun Liu 2 and Timothy R. Hoover 3,* 1 Department of Microbiology, Cornell University, Ithaca, NY 14853, USA; [email protected] 2 Microbial Sciences Institute, Department of Microbial Pathogenesis, Yale University, West Haven, CT 06516, USA; [email protected] 3 Department of Microbiology, University of Georgia, Athens, GA 30602, USA * Correspondence: [email protected]; Tel.: +1-706-542-2675 Received: 30 January 2020; Accepted: 26 February 2020; Published: 27 February 2020 Abstract: A number of Gram-negative bacteria have a membrane surrounding their flagella, referred to as the flagellar sheath, which is continuous with the outer membrane. The flagellar sheath was initially described in Vibrio metschnikovii in the early 1950s as an extension of the outer cell wall layer that completely surrounded the flagellar filament. Subsequent studies identified other bacteria that possess flagellar sheaths, most of which are restricted to a few genera of the phylum Proteobacteria. Biochemical analysis of the flagellar sheaths from a few bacterial species revealed the presence of lipopolysaccharide, phospholipids, and outer membrane proteins in the sheath. Some proteins localize preferentially to the flagellar sheath, indicating mechanisms exist for protein partitioning to the sheath. Recent cryo-electron tomography studies have yielded high resolution images of the flagellar sheath and other structures closely associated with the sheath, which has generated insights and new hypotheses for how the flagellar sheath is synthesized. Various functions have been proposed for the flagellar sheath, including preventing disassociation of the flagellin subunits in the presence of gastric acid, avoiding activation of the host innate immune response by flagellin, activating the host immune response, adherence to host cells, and protecting the bacterium from bacteriophages.
    [Show full text]