DOCSLIB.ORG

Calculating the Structure-Based Phylogenetic Relationship

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Calculating the Structure-Based Phylogenetic Relationship CALCULATING THE STRUCTURE-BASED PHYLOGENETIC RELATIONSHIP OF DISTANTLY RELATED HOMOLOGOUS PROTEINS UTILIZING MAXIMUM LIKELIHOOD STRUCTURAL ALIGNMENT COMBINATORICS AND A NOVEL STRUCTURAL MOLECULAR CLOCK HYPOTHESIS A DISSERTATION IN Molecular Biology and Biochemistry and Cell Biology and Biophysics Presented to the Faculty of the University of Missouri-Kansas City in partial fulfillment of the requirements for the degree Doctor of Philosophy by SCOTT GARRETT FOY B.S., Southwest Baptist University, 2005 B.A., Truman State University, 2007 M.S., University of Missouri-Kansas City, 2009 Kansas City, Missouri 2013 © 2013 SCOTT GARRETT FOY ALL RIGHTS RESERVED CALCULATING THE STRUCTURE-BASED PHYLOGENETIC RELATIONSHIP OF DISTANTLY RELATED HOMOLOGOUS PROTEINS UTILIZING MAXIMUM LIKELIHOOD STRUCTURAL ALIGNMENT COMBINATORICS AND A NOVEL STRUCTURAL MOLECULAR CLOCK HYPOTHESIS Scott Garrett Foy, Candidate for the Doctor of Philosophy Degree University of Missouri-Kansas City, 2013 ABSTRACT Dendrograms establish the evolutionary relationships and homology of species, proteins, or genes. Homology modeling, ligand binding, and pharmaceutical testing all depend upon the homology ascertained by dendrograms. Regardless of the specific algorithm, all dendrograms that ascertain protein evolutionary homology are generated utilizing polypeptide sequences. However, because protein structures superiorly conserve homology and contain more biochemical information than their associated protein sequences, I hypothesize that utilizing the structure of a protein instead of its sequence will generate a superior dendrogram. Generating a dendrogram utilizing protein structure requires a unique methodology and novel bioinformatic programs to implement this methodology. Contained within this dissertation is an original methodology that permits the aforementioned structure-based iii dendrogram generation hypothesis. Additionally, I have scripted three novel bioinformatics programs required by this proposed methodology: a protein structure alignment program that proficiently superimposes distant homologs, an accurate structure-dependent sequence alignment program, and a dendrogram generation program that employs a novel structural molecular clock hypothesis. The results from this methodology support the proposed hypothesis by demonstrating that generating dendrograms utilizing protein structures is superior to those generated utilizing exclusively protein sequences. iv APPROVAL PAGE The faculty listed below, appointed by the Dean of the School of Graduate Studies, have examined a dissertation titled “Calculating the Structure-based Phylogenetic Relationship of Distantly Related Homologous Proteins Utilizing Maximum Likelihood Structural Alignment Combinatorics and a Novel Structural Molecular Clock Hypothesis”, presented by Scott G. Foy, candidate for the Doctor of Philosophy degree, and certify that in their opinion it is worthy of acceptance. Supervisory Committee Gerald Wyckoff, Ph.D., Committee Chair Department of Molecular Biology and Biochemistry Samuel Bouyain, Ph.D. Department of Molecular Biology and Biochemistry John Laity, Ph.D. Department of Cell Biology and Biophysics Thomas Menees, Ph.D. Department of Cell Biology and Biophysics Jakob Waterborg, Ph.D. Department of Cell Biology and Biophysics v CONTENTS ABSTRACT ........................................................................................................................ iii LIST OF ILLUSTRATIONS ................................................................................................. x LIST OF TABLES ..............................................................................................................xii ACKNOWLEDGEMENTS ............................................................................................... xiii Chapter I. INTRODUCTION ..................................................................................................... 1 The Molecular Clock and Dendrogram Generation ......................................... 4 Implementation of Sequence and Structure in Dendrogram Generation ................................................................................. 5 Methodological and Algorithmic Overview .................................................... 7 Methodological Overview ................................................................... 7 Novel Algorithms ............................................................................... 8 II. SABLE: STRUCTURAL ALIGNMENT BY MAXIMUM LIKELIHOOD ESTIMATION ......................................................................................................... 16 Introduction to Structural Alignment and Superpositioning .......................... 16 Structural Alignment and Superpositioning Algorithms .................... 17 Superpositioning by Structural Alignment ........................................ 20 SABLE Algorithm ....................................................................................... 22 Maximum Likelihood Structural Alignment ...................................... 22 Protein Geometry Terminology ......................................................... 23 Superimposing Measurement of SABLE ........................................... 24 Subunit Homology ............................................................................ 29 Reducing Infinite Pseudostates ......................................................... 30 vi SABLE: Phase 1 ............................................................................... 31 SABLE: Phase 2 ............................................................................... 33 SABLE: Phase 3 ............................................................................... 34 SABLE Accuracy Program ............................................................... 35 SABLE Results ............................................................................................ 36 Measuring Structural Superimposition .............................................. 37 Monomeric Pairwise Comparison ..................................................... 39 Multisubunit Pairwise Comparison ................................................... 49 Multiple Alignment Comparison ....................................................... 50 Discussion of SABLE Results ...................................................................... 56 III. UNITS: UNIVERSAL TRUE SDSA (STRUCTURE-DEPENDENT SEQUENCE ALIGNMENT) ........................................................................................................ 57 Introduction to Structure-dependent Sequence Alignments ........................... 57 Current SDSA Limitations ................................................................ 58 The UniTS Solution .......................................................................... 60 UniTS Algorithm ......................................................................................... 62 Pairwise SDSA ................................................................................. 62 The Grid ........................................................................................... 66 Residue Determination...................................................................... 67 Multiple SDSA ................................................................................. 68 Multiple SDSA Quality Assessment: Mean Standard Deviation ........................................................................... 70 UniTS Results .............................................................................................. 71 UniTS Compared to Theseus ............................................................ 72 vii UniTS Compared to DALI ................................................................ 74 UniTS Compared to Chimera ............................................................ 75 Multiple PL/PG SDSA ...................................................................... 77 Discussion of UniTS Results ........................................................................ 81 IV. PUSH: PHYLOGENETIC TREE USING STRUCTURAL HOMOLOGY .............. 82 Structural Molecular Clock Hypothesis ........................................................ 83 The Application of the Sequence-based Molecular Clock Hypothesis to Protein Structure............................................... 83 Proposal of a New Evolutionary Mechanism for Use as a Molecular Clock ............................................................ 85 Protein Structural and Function Evolution......................................... 87 Correlating Protein Structural Divergence and Evolutionary Selection Pressure ................................................. 88 A Novel Structural Molecular Clock Hypothesis ............................... 90 Structural Molecular Clock Hypothesis Discussion ........................... 92 PUSH Algorithm .......................................................................................... 93 Derivation of the Evolutionary Distance Matrix ................................ 93 Hierarchical Clustering ..................................................................... 94 PUSH Results .............................................................................................. 97 Quad PG-PL Dendrograms ............................................................... 97 Penta-PG Dendrograms .................................................................. 101 V. PUSH DISCUSSION AND GENERAL CONCLUSION ....................................... 105 Appendix
Load more

Recommended publications
  • Relationship Between Sequence Homology, Genome Architecture, and Meiotic Behavior of the Sex Chromosomes in North American Voles
    HIGHLIGHTED ARTICLE | INVESTIGATION Relationship Between Sequence Homology, Genome Architecture, and Meiotic Behavior of the Sex Chromosomes in North American Voles Beth L. Dumont,*,1,2 Christina L. Williams,† Bee Ling Ng,‡ Valerie Horncastle,§ Carol L. Chambers,§ Lisa A. McGraw,** David Adams,‡ Trudy F. C. Mackay,*,**,†† and Matthew Breen†,†† *Initiative in Biological Complexity, †Department of Molecular Biomedical Sciences, College of Veterinary Medicine, **Department of Biological Sciences, and ††Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 04609, ‡Cytometry Core Facility, Wellcome Sanger Institute, Hinxton, United Kingdom, CB10 1SA and §School of Forestry, Northern Arizona University, Flagstaff, Arizona 86011 ORCID ID: 0000-0003-0918-0389 (B.L.D.) ABSTRACT In most mammals, the X and Y chromosomes synapse and recombine along a conserved region of homology known as the pseudoautosomal region (PAR). These homology-driven interactions are required for meiotic progression and are essential for male fertility. Although the PAR fulfills key meiotic functions in most mammals, several exceptional species lack PAR-mediated sex chromosome associations at meiosis. Here, we leveraged the natural variation in meiotic sex chromosome programs present in North American voles (Microtus) to investigate the relationship between meiotic sex chromosome dynamics and X/Y sequence homology. To this end, we developed a novel, reference-blind computational method to analyze sparse sequencing data from flow- sorted X and Y chromosomes isolated from vole species with sex chromosomes that always (Microtus montanus), never (Microtus mogollonensis), and occasionally synapse (Microtus ochrogaster) at meiosis. Unexpectedly, we find more shared X/Y homology in the two vole species with no and sporadic X/Y synapsis compared to the species with obligate synapsis.
    [Show full text]
  • Molecular Homology and Multiple-Sequence Alignment: an Analysis of Concepts and Practice
    CSIRO PUBLISHING Australian Systematic Botany, 2015, 28,46–62 LAS Johnson Review http://dx.doi.org/10.1071/SB15001 Molecular homology and multiple-sequence alignment: an analysis of concepts and practice David A. Morrison A,D, Matthew J. Morgan B and Scot A. Kelchner C ASystematic Biology, Uppsala University, Norbyvägen 18D, Uppsala 75236, Sweden. BCSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia. CDepartment of Biology, Utah State University, 5305 Old Main Hill, Logan, UT 84322-5305, USA. DCorresponding author. Email: [email protected] Abstract. Sequence alignment is just as much a part of phylogenetics as is tree building, although it is often viewed solely as a necessary tool to construct trees. However, alignment for the purpose of phylogenetic inference is primarily about homology, as it is the procedure that expresses homology relationships among the characters, rather than the historical relationships of the taxa. Molecular homology is rather vaguely defined and understood, despite its importance in the molecular age. Indeed, homology has rarely been evaluated with respect to nucleotide sequence alignments, in spite of the fact that nucleotides are the only data that directly represent genotype. All other molecular data represent phenotype, just as do morphology and anatomy. Thus, efforts to improve sequence alignment for phylogenetic purposes should involve a more refined use of the homology concept at a molecular level. To this end, we present examples of molecular-data levels at which homology might be considered, and arrange them in a hierarchy. The concept that we propose has many levels, which link directly to the developmental and morphological components of homology.
    [Show full text]
  • Eukaryotic Genome Annotation
    Comparative Features of Multicellular Eukaryotic Genomes (2017) (First three statistics from www.ensembl.org; other from original papers) C. elegans A. thaliana D. melanogaster M. musculus H. sapiens Species name Nematode Thale Cress Fruit Fly Mouse Human Size (Mb) 103 136 143 3,482 3,555 # Protein-coding genes 20,362 27,655 13,918 22,598 20,338 (25,498 (13,601 original (30,000 (30,000 original est.) original est.) original est.) est.) Transcripts 58,941 55,157 34,749 131,195 200,310 Gene density (#/kb) 1/5 1/4.5 1/8.8 1/83 1/97 LINE/SINE (%) 0.4 0.5 0.7 27.4 33.6 LTR (%) 0.0 4.8 1.5 9.9 8.6 DNA Elements 5.3 5.1 0.7 0.9 3.1 Total repeats 6.5 10.5 3.1 38.6 46.4 Exons % genome size 27 28.8 24.0 per gene 4.0 5.4 4.1 8.4 8.7 average size (bp) 250 506 Introns % genome size 15.6 average size (bp) 168 Arabidopsis Chromosome Structures Sorghum Whole Genome Details Characterizing the Proteome The Protein World • Sequencing has defined o Many, many proteins • How can we use this data to: o Define genes in new genomes o Look for evolutionarily related genes o Follow evolution of genes ▪ Mixing of domains to create new proteins o Uncover important subsets of genes that ▪ That deep phylogenies • Plants vs. animals • Placental vs. non-placental animals • Monocots vs. dicots plants • Common nomenclature needed o Ensure consistency of interpretations InterPro (http://www.ebi.ac.uk/interpro/) Classification of Protein Families • Intergrated documentation resource for protein super families, families, domains and functional sites o Mitchell AL, Attwood TK, Babbitt PC, et al.
    [Show full text]
  • Molecular Homology and Multiple-Sequence Alignment: an Analysis of Concepts and Practice
    CSIRO PUBLISHING Australian Systematic Botany, 2015, 28, 46–62 LAS Johnson Review http://dx.doi.org/10.1071/SB15001 Molecular homology and multiple-sequence alignment: an analysis of concepts and practice David A. Morrison A,D, Matthew J. Morgan B and Scot A. Kelchner C ASystematic Biology, Uppsala University, Norbyvägen 18D, Uppsala 75236, Sweden. BCSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia. CDepartment of Biology, Utah State University, 5305 Old Main Hill, Logan, UT 84322-5305, USA. DCorresponding author. Email: [email protected] Abstract. Sequence alignment is just as much a part of phylogenetics as is tree building, although it is often viewed solely as a necessary tool to construct trees. However, alignment for the purpose of phylogenetic inference is primarily about homology, as it is the procedure that expresses homology relationships among the characters, rather than the historical relationships of the taxa. Molecular homology is rather vaguely defined and understood, despite its importance in the molecular age. Indeed, homology has rarely been evaluated with respect to nucleotide sequence alignments, in spite of the fact that nucleotides are the only data that directly represent genotype. All other molecular data represent phenotype, just as do morphology and anatomy. Thus, efforts to improve sequence alignment for phylogenetic purposes should involve a more refined use of the homology concept at a molecular level. To this end, we present examples of molecular-data levels at which homology might be considered, and arrange them in a hierarchy. The concept that we propose has many levels, which link directly to the developmental and morphological components of homology.
    [Show full text]
  • STACK: a Toolkit for Analysing Β-Helix Proteins
    STACK: a toolkit for analysing ¯-helix proteins Master of Science Thesis (20 points) Salvatore Cappadona, Lars Diestelhorst Abstract ¯-helix proteins contain a solenoid fold consisting of repeated coils forming parallel ¯-sheets. Our goal is to formalise the intuitive notion of a ¯-helix in an objective algorithm. Our approach is based on first identifying residues stacks — linear spatial arrangements of residues with similar conformations — and then combining these elementary patterns to form ¯-coils and ¯-helices. Our algorithm has been implemented within STACK, a toolkit for analyzing ¯-helix proteins. STACK distinguishes aromatic, aliphatic and amidic stacks such as the asparagine ladder. Geometrical features are computed and stored in a relational database. These features include the axis of the ¯-helix, the stacks, the cross-sectional shape, the area of the coils and related packing information. An interface between STACK and a molecular visualisation program enables structural features to be highlighted automatically. i Contents 1 Introduction 1 2 Biological Background 2 2.1 Basic Concepts of Protein Structure ....................... 2 2.2 Secondary Structure ................................ 2 2.3 The ¯-Helix Fold .................................. 3 3 Parallel ¯-Helices 6 3.1 Introduction ..................................... 6 3.2 Nomenclature .................................... 6 3.2.1 Parallel ¯-Helix and its ¯-Sheets ..................... 6 3.2.2 Stacks ................................... 8 3.2.3 Coils ..................................... 8 3.2.4 The Core Region .............................. 8 3.3 Description of Known Structures ......................... 8 3.3.1 Helix Handedness .............................. 8 3.3.2 Right-Handed Parallel ¯-Helices ..................... 13 3.3.3 Left-Handed Parallel ¯-Helices ...................... 19 3.4 Amyloidosis .................................... 20 4 The STACK Toolkit 24 4.1 Identification of Structural Elements ....................... 24 4.1.1 Stacks ...................................
    [Show full text]
  • Extensive Intragenic Sequence Homology in Two Distinct Rat Lens Y
    Proc. NatL Acad. Sci. USA Vol. 79, pp. 6876-6880, November 1982 Biochemistry Extensive intragenic sequence homology in two distinct rat lens y-crystallin cDNAs suggests duplications of a primordial gene (cDNA cloning/DNA sequence determination/gene generation) ROB J. M. MOORMANN*, JOHAN T. DEN DUNNEN*, HANS BLOEMENDALt, AND JOHN G. G. SCHOENMAKERS* Departments of *Molecular Biology and tBiochemistry, University of Nijmegen, Nijmegen, 6525 ED, The Netherlands Communicated by L. N. M. Duysens, August 17, 1982 ABSTRACT The nucleotide sequences of two different rat formation and intermolecular interactions of the crystallins in lens y-crystallin cDNA clones, pRLy2 and pRLy3, have been the lens fiber cells in situ. However, it has been established that determined. pRLy3 contains the complete coding information for increased light scattering ofsenile nuclear cataract is associated a y-crystallin of 173 amino acids whereas pRLy2 is incomplete in with denaturation, chemical crosslinking including disulfide that it lacks the codons for the first three amino acids ofa separate bridges, and aggregation of crystallins (15). but very homologous y-crystallin of identical length. Both rat y- Recently, the three-dimensional structure of a lens protein, crystallins are homologous to the known amino acid sequence of bovine y-crystallin II, has been determined by Blundell et aL bovine y-crystallin II which is only a single amino acid longer. The (16). It turned out that the sulfhydryl groups in the highly sym- length of the region downstream the coding sequence to the A-A- 3 T-A-A-A polyadenylylation signal sequence is 40 nucleotides in metric two-domain structure of this protein were found in each clone.
    [Show full text]
  • And Beta-Helical Protein Motifs
    Soft Matter Mechanical Unfolding of Alpha- and Beta-helical Protein Motifs Journal: Soft Matter Manuscript ID SM-ART-10-2018-002046.R1 Article Type: Paper Date Submitted by the 28-Nov-2018 Author: Complete List of Authors: DeBenedictis, Elizabeth; Northwestern University Keten, Sinan; Northwestern University, Mechanical Engineering Page 1 of 10 Please doSoft not Matter adjust margins Soft Matter ARTICLE Mechanical Unfolding of Alpha- and Beta-helical Protein Motifs E. P. DeBenedictis and S. Keten* Received 24th September 2018, Alpha helices and beta sheets are the two most common secondary structure motifs in proteins. Beta-helical structures Accepted 00th January 20xx merge features of the two motifs, containing two or three beta-sheet faces connected by loops or turns in a single protein. Beta-helical structures form the basis of proteins with diverse mechanical functions such as bacterial adhesins, phage cell- DOI: 10.1039/x0xx00000x puncture devices, antifreeze proteins, and extracellular matrices. Alpha helices are commonly found in cellular and extracellular matrix components, whereas beta-helices such as curli fibrils are more common as bacterial and biofilm matrix www.rsc.org/ components. It is currently not known whether it may be advantageous to use one helical motif over the other for different structural and mechanical functions. To better understand the mechanical implications of using different helix motifs in networks, here we use Steered Molecular Dynamics (SMD) simulations to mechanically unfold multiple alpha- and beta- helical proteins at constant velocity at the single molecule scale. We focus on the energy dissipated during unfolding as a means of comparison between proteins and work normalized by protein characteristics (initial and final length, # H-bonds, # residues, etc.).
    [Show full text]
  • Introduction to the Local Theory of Curves and Surfaces
    Introduction to the Local Theory of Curves and Surfaces Notes of a course for the ICTP-CUI Master of Science in Mathematics Marco Abate, Fabrizio Bianchi (with thanks to Francesca Tovena) (Much more on this subject can be found in [1]) CHAPTER 1 Local theory of curves Elementary geometry gives a fairly accurate and well-established notion of what is a straight line, whereas is somewhat vague about curves in general. Intuitively, the difference between a straight line and a curve is that the former is, well, straight while the latter is curved. But is it possible to measure how curved a curve is, that is, how far it is from being straight? And what, exactly, is a curve? The main goal of this chapter is to answer these questions. After comparing in the first two sections advantages and disadvantages of several ways of giving a formal definition of a curve, in the third section we shall show how Differential Calculus enables us to accurately measure the curvature of a curve. For curves in space, we shall also measure the torsion of a curve, that is, how far a curve is from being contained in a plane, and we shall show how curvature and torsion completely describe a curve in space. 1.1. How to define a curve n What is a curve (in a plane, in space, in R )? Since we are in a mathematical course, rather than in a course about military history of Prussian light cavalry, the only acceptable answer to such a question is a precise definition, identifying exactly the objects that deserve being called curves and those that do not.
    [Show full text]
  • Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
    BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher.
    [Show full text]
  • BLAST Exercise: Detecting and Interpreting Genetic Homology
    Last Update: 08/10/2021 BLAST Exercise: Detecting and Interpreting Genetic Homology Adapted by Taylor Cordonnier, Chris Shaffer, Wilson Leung and Sarah C.R. Elgin from Detecting and Interpreting Genetic Homology by Dr. J eremy Buhler Recommended background tutorial An Introduction to NCBI BLAST Resources NCBI BLAST is available at https://blast.ncbi.nlm.nih.gov/Blast.cgi The RepeatMasker web server is available at http://repeatmasker.org/cgi-bin/WEBRepeatMasker The UniProt Protein Knowledgebase is available at https://www.uniprot.org/ Files for this exercise The package containing the files for this exercise is available through the “Detecting and Interpreting Genetic Homology” page on the GEP website. Introduction The Basic Local Alignment Search Tool (BLAST) is a program that reports regions of local similarity (at either the nucleotide or protein level) between a query sequence and sequences within a database. The ability to detect sequence homology allows us to determine if a gene or a protein is related to other known genes or proteins. Detecting sequence homology also facilitates the identification of conserved domains that are shared by multiple genes and the identification of members of a gene family. BLAST is popular because it can efficiently identify regions of local similarity between two sequences. More importantly, BLAST is based on a robust statistical framework. This framework allows BLAST to determine if the alignment between two sequences is statistically significant (i.e. the probability of obtaining an alignment this good by chance is low). Before proceeding with annotation, it is important to understand the inferences that we are making when we use BLAST in our analysis.
    [Show full text]
  • Evolution of the Muscarinic Acetylcholine Receptors in Vertebrates and Dan Larhammar ء,Christina A
    New Research Development Evolution of the Muscarinic Acetylcholine Receptors in Vertebrates and Dan Larhammar ء,Christina A. Bergqvist ء,Julia E. Pedersen https://doi.org/10.1523/ENEURO.0340-18.2018 Department of Neuroscience, Unit of Pharmacology, Science for Life Laboratory, Uppsala University, Uppsala SE-751 24, Sweden Abstract The family of muscarinic acetylcholine receptors (mAChRs) consists of five members in mammals, encoded by the CHRM1-5 genes. The mAChRs are G-protein-coupled receptors, which can be divided into the following two subfamilies: M2 and M4 receptors coupling to Gi/o; and M1, M3, and M5 receptors coupling to Gq/11. However, despite the fundamental roles played by these receptors, their evolution in vertebrates has not yet been fully described. We have combined sequence-based phylogenetic analyses with comparisons of exon–intron organi- zation and conserved synteny in order to deduce the evolution of the mAChR receptors. Our analyses verify the existence of two ancestral genes prior to the two vertebrate tetraploidizations (1R and 2R). After these events, one gene had duplicated, resulting in CHRM2 and CHRM4; and the other had triplicated, forming the CHRM1, CHRM3, and CHRM5 subfamily. All five genes are still present in all vertebrate groups investigated except the CHRM1 gene, which has not been identified in some of the teleosts or in chicken or any other birds. Interestingly, the third tetraploidization (3R) that took place in the teleost predecessor resulted in duplicates of all five mAChR genes of which all 10 are present in zebrafish. One of the copies of the CHRM2 and CHRM3 genes and both CHRM4 copies have gained introns in teleosts.
    [Show full text]
  • Nucleic Acids Research
    Volufme 9 Number 21 1981 Voum 9 ubr2 91NcecAisRsacNucleic Acids Research Different nuckeotide changes in the hlge rRNA gene of the mitochondrial DNA confer chloramph- enicol resistance on two human cell lnes Hugues Blanc, Camellia W.Adams and Douglas C.Wallace Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA Received 3 August 1981 ABSTRACT The nucleotide sequence of the mitochondrial DNA (mtDNA) in the region coding for the 3' end of the large rRNA has been determined for two human cell lines bearing independent cytoplasmic chloramphenicol-resistant (CAP-r) mutations. Comparison of the sequences of these two phenotypically different CAP-r mutants with their CAP-sensitive (CAP-s) parental cell lines has revealed a single base change for each in a region which is highly conserved among species. One CAP-r mutation is associated with an A to G transition on the coding strand while the second contains a G to T transversion 52 nucleotides away. Comparable sequence changes in this region had previously been found for mouse and yeast cell mitochondrial CAP-r mutants. Thus, changes in the large rRNA gene eliminate the inhibition of the ribosome by CAP and different nucleotide changes may result in variations in the drug-r phenotype. INTRODUCTION The human mitochondrial DNA (mtDNA) is 16,569 base pairs (bp) in length and codes for two ribosomal RNAs (rRNAs), 22 transfer RNAs (tRNAs), and 13 polypeptides, 5 of which have been assigned a genetic function (1). Virtually all of the genes including the 12S and 16S rRNA genes are separated by one or more tRNAs.
    [Show full text]