Evolutionary Dynamics of Recent Selection on Cognitive Abilities
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Systematics of Polistes (Hymenoptera: Vespidae), with a Phylogenetic Consideration of Hamilton’S Haplodiploidy Hypothesis
Ann. Zool. Fennici 43: 390–406 ISSN 0003-455X Helsinki 29 December 2006 © Finnish Zoological and Botanical Publishing Board 2006 Systematics of Polistes (Hymenoptera: Vespidae), with a phylogenetic consideration of Hamilton’s haplodiploidy hypothesis Kurt M. Pickett*, James M. Carpenter & Ward C. Wheeler Division of Invertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10023, USA * Current address: Department of Biology, University of Vermont, Room 120A Marsh Life Science Building, 109 Carrigan Drive, Burlington, VT 05405, USA Received 30 Nov. 2005, revised version received 21 Nov. 2006, accepted 4 May 2006 Pickett, K. M., Carpenter, J. M. & Wheeler, W. C. 2006: Systematics of Polistes (Hymenoptera: Vespidae), with a phylogenetic consideration of Hamilton’s haplodiploidy hypothesis. — Ann. Zool. Fennici 43: 390–406. A review of previously published cladistic analyses of Polistes is presented. The two most recent analyses of Polistes are shown to be largely consistent phylogenetically. Although the taxonomy implied by each differs, this difference is shown to be mostly due to taxon sampling. After the review, a phylogenetic analysis of Polistes — the most data-rich yet undertaken — is presented. The analysis includes new data and the data from previously published analyses. The differing conclusions of the previous studies are discussed in light of the new analysis. After discussing the status of subge- neric taxonomy in Polistes, the new phylogeny is used to test an important hypothesis regarding the origin of social behavior: the haplodiploidy hypothesis of Hamilton. Prior phylogenetic analyses so while these studies achieved their goal, with within Polistes resolutions leading to rejection of Emery’s Rule, they had little to say about broader phylogenetic Cladistic analysis of species-level relationships patterns within the genus. -
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microorganisms Article Label-Free Quantitative Proteomics Analysis in Susceptible and Resistant Brassica napus Cultivars Infected with Xanthomonas campestris pv. campestris Md Tabibul Islam 1,2, Bok-Rye Lee 1,3 , Van Hien La 1, Dong-Won Bae 4, Woo-Jin Jung 5 and Tae-Hwan Kim 1,* 1 Department of Animal Science, Institute of Agricultural Science and Technology, College of Agriculture & Life Science, Chonnam National University, Gwangju 61186, Korea; [email protected] (M.T.I.); [email protected] (B.-R.L.); [email protected] (V.H.L.) 2 Alson H. Smith Jr. Agricultural Research and Extension Center, School of Plant and Environmental Sciences, Virginia Tech, Winchester, VA 22602, USA 3 Asian Pear Research Institute, Chonnam National University, Gwangju 61186, Korea 4 Central Instrument Facility, Gyeongsang National University, Jinju 52828, Korea; [email protected] 5 Division of Applied Bioscience and Biotechnology, Institute of Environmentally Friendly Agriculture (IEFA), College of Agriculture and Life Science, Chonnam National University, Gwangju 61186, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-62-530-2126 Abstract: Black rot, caused by Xanthomonas campestris pv. campestris (Xcc), is the main disease of cruciferous vegetables. To characterize the resistance mechanism in the Brassica napus–Xcc pathosys- tem, Xcc-responsive proteins in susceptible (cv. Mosa) and resistant (cv. Capitol) cultivars were investigated using gel-free quantitative proteomics and analysis of gene expression. This allowed us to identify 158 and 163 differentially expressed proteins following Xcc infection in cv. Mosa and cv. Citation: Islam, M.T.; Lee, B.-R.; Capitol, respectively, and to classify them into five major categories including antioxidative systems, La, V.H.; Bae, D.-W.; Jung, W.-J.; proteolysis, photosynthesis, redox, and innate immunity. -
PANNZER 2: Annotate a Complete Proteome in Minutes!
PANNZER 2: Annotate a complete proteome in minutes! Alan J Medlar#, Petri Törönen#, Elaine Zosa, Liisa Holm* Structural Genomics Group, Institute of Biotechnology PO Box 56, 00014 University of Helsinki, Finland #These authors contributed equally *To whom correspondence should be addressed: [email protected] 1. INTRODUCTION As high-throughput sequencing has become increasingly efficient, downstream analysis has become the main bottleneck in genome sequencing projects. For example, a critical and time-consuming step is the annotation of the organism’s proteome: assigning molecular functions, involvement in biological processes and the cellular localizations of all identified protein sequences. The process of protein annotation involves classifying each protein using the Gene Ontology (GO) and selecting suitable free text descriptions, which are required for submission into sequence databases. We refer to these as GO and DE predictions, respectively. Although servers are available that perform functional annotation, none perform both GO and DE predictions concurrently, they tend to be slow and are restricted in the number of queries that can be submitted. Finally, many of the existing methods do not return an estimate of the prediction accuracy. 2. METHODS AND RESULTS The above shortcomings are remedied by PANNZER 2, an interactive web server and standalone program for protein function prediction. It is, to our knowledge, the only tool that performs both DE and GO prediction. It uses SANS-parallel [1], a high-throughput sequence search program that is thousands of times faster than BLAST, to identify homologous sequences. It is, therefore, capable of analyzing tens of thousands of proteins interactively. Results are displayed as an HTML table summarizing both DE and GO predictions, including a statistical estimate for the reliability of each prediction. -
Pan-Genome-Wide Analysis of Pantoea Ananatis Identified Genes Linked to Pathogenicity In
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.24.219337; this version posted July 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Pan-genome-wide analysis of Pantoea ananatis identified genes linked to pathogenicity in onion Gaurav Agarwal1*, Divya Choudhary1, Shaun P. Stice2, Brendon K. Myers1, Ronald D. Gitaitis1, Stephanus N. Venter3, Brian H. Kvitko2, Bhabesh Dutta1* 1Department of Plant Pathology, Coastal Plain Experimental Station, University of Georgia, Tifton, Georgia, USA 2Department of Plant Pathology, University of Georgia, Athens, Georgia, USA 3Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria, South Africa *Corresponding authors: [email protected] (BD); [email protected] (GA) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.24.219337; this version posted July 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Pantoea ananatis is a member of a Pantoea spp. complex that causes center rot of onion, which significantly affects onion yield and quality. This pathogen does not have typical virulence factors like type II or type III secretion systems but appears to require a biosynthetic gene-cluster, HiVir/PASVIL (located chromosomally), for a phosphonate secondary metabolite, and the onion- virulence regions, OVR (localized on a megaplasmid), for onion pathogenicity and virulence, respectively. -
Workflows for Rapid Functional Annotation of Diverse
insects Article Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes Surya Saha 1,2 , Amanda M. Cooksey 2,3, Anna K. Childers 4 , Monica F. Poelchau 5 and Fiona M. McCarthy 2,* 1 Boyce Thompson Institute, 533 Tower Rd., Ithaca, NY 14853, USA; [email protected] 2 School of Animal and Comparative Biomedical Sciences, University of Arizona, 1117 E. Lowell St., Tucson, AZ 85721, USA; [email protected] 3 CyVerse, BioScience Research Laboratories, University of Arizona, 1230 N. Cherry Ave., Tucson, AZ 85721, USA 4 Bee Research Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, USDA, 10300 Baltimore Ave., Beltsville, MD 20705, USA; [email protected] 5 National Agricultural Library, Agricultural Research Service, USDA, 10301 Baltimore Ave., Beltsville, MD 20705, USA; [email protected] * Correspondence: fi[email protected] Simple Summary: Genomic technologies are accumulating information about genes faster than ever before, and sequencing initiatives, such as the Earth BioGenome Project, i5k, and Ag100Pest Initiative, are expected to increase this rate of acquisition. However, if genomic sequencing is to be used for the improvement of human health, agriculture, and our understanding of biological systems, it is necessary to identify genes and understand how they contribute to biological outcomes. While there are several well-established workflows for assembling genomic sequences and identifying genes, understanding gene function is essential to create actionable knowledge. Moreover, this functional annotation process must be easily accessible and provide information at a genomic scale to keep up Citation: Saha, S.; Cooksey, A.M.; with new sequence data. We report a well-defined workflow for rapid functional annotation of whole Childers, A.K.; Poelchau, M.F.; proteomes to produce Gene Ontology and pathways information. -
Ecological and Evolutionary Dynamics of Interconnectedness and Modularity
Ecological and evolutionary dynamics of interconnectedness and modularity Jan M. Nordbottena,b, Simon A. Levinc, Eörs Szathmáryd,e,f, and Nils C. Stensethg,1 aDepartment of Mathematics, University of Bergen, N-5020 Bergen, Norway; bDepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544; cDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; dParmenides Center for the Conceptual Foundations of Science, D-82049 Pullach, Germany; eMTA-ELTE (Hungarian Academy of Sciences—Eötvös University), Theoretical Biology and Evolutionary Ecology Research Group, Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös University, Budapest, H-1117 Hungary; fEvolutionary Systems Research Group, MTA (Hungarian Academy of Sciences) Ecological Research Centre, Tihany, H-8237 Hungary; and gCentre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway Contributed by Nils C. Stenseth, December 1, 2017 (sent for review September 12, 2017; reviewed by David C. Krakauer and Günter P. Wagner) In this contribution, we develop a theoretical framework for linking refinement of this inquiry is to look for compartmentalization (i.e., microprocesses (i.e., population dynamics and evolution through modularity) in ecosystems. In food webs, for example, compartments natural selection) with macrophenomena (such as interconnectedness (which are subgroups of taxa with many strong interactions in each and modularity within an -
What Genomic Data Can Reveal About Eco-Evolutionary Dynamics
PERSPECTIVE https://doi.org/10.1038/s41559-017-0385-2 What genomic data can reveal about eco-evolutionary dynamics Seth M. Rudman 1*, Matthew A. Barbour2, Katalin Csilléry3, Phillip Gienapp4, Frederic Guillaume2, Nelson G. Hairston Jr5, Andrew P. Hendry6, Jesse R. Lasky7, Marina Rafajlović8,9, Katja Räsänen10, Paul S. Schmidt1, Ole Seehausen 11,12, Nina O. Therkildsen13, Martin M. Turcotte3,14 and Jonathan M. Levine15 Recognition that evolution operates on the same timescale as ecological processes has motivated growing interest in eco-evo- lutionary dynamics. Nonetheless, generating sufficient data to test predictions about eco-evolutionary dynamics has proved challenging, particularly in natural contexts. Here we argue that genomic data can be integrated into the study of eco-evo- lutionary dynamics in ways that deepen our understanding of the interplay between ecology and evolution. Specifically, we outline five major questions in the study of eco-evolutionary dynamics for which genomic data may provide answers. Although genomic data alone will not be sufficient to resolve these challenges, integrating genomic data can provide a more mechanistic understanding of the causes of phenotypic change, help elucidate the mechanisms driving eco-evolutionary dynamics, and lead to more accurate evolutionary predictions of eco-evolutionary dynamics in nature. vidence that the ways in which organisms interact with their evolution plays a central role in regulating such dynamics. environment can evolve fast enough to alter ecological dynam- Particularly relevant is information gleaned from efforts to under- Eics has forged a new link between ecology and evolution1–5. A stand the genomic basis of adaptation, a burgeoning field in evolu- growing area of study, termed eco-evolutionary dynamics, centres tionary biology that investigates the specific genomic underpinnings on understanding when rapid evolutionary change is a meaningful of phenotypic variation, its response to natural selection and effects driver of ecological dynamics in natural ecosystems6–8. -
How the Energy Sensor, AMPK, Impact the Testicular Function Pascal Froment, Mélanie Faure, Edith Guibert, Jean-Pierre Brillard
How the energy sensor, AMPK, impact the testicular function Pascal Froment, Mélanie Faure, Edith Guibert, Jean-Pierre Brillard To cite this version: Pascal Froment, Mélanie Faure, Edith Guibert, Jean-Pierre Brillard. How the energy sensor, AMPK, impact the testicular function. 27. Conference of European Comparative Endocrinologists, Aug 2014, Rennes, France. 2014. hal-02742016 HAL Id: hal-02742016 https://hal.inrae.fr/hal-02742016 Submitted on 3 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright 27th Conference of European Comparative Endocrinologists CECE 2014 25-29 August 2014 Rennes, France 3 27th Conference of European Comparative Endocrinologists Organized with the generous support and help of our sponsors Université de Rennes 1 European Society for Comparative Endocrinology (Grants) European Union INTEREG TC2N Rennes Métropole European Society of Endocrinology (Grants) Institut National de la Recherche Agronomique Société de Neuroendocrinologie (Grants) Institut National de l'Environnement Industriel et des Risques !"#$%$&$'()'*)+,)*+,)'#&*'-.'#."$/ -
Martin A. Nowak
EVOLUTIONARY DYNAMICS EXPLORING THE EQUATIONS OF LIFE MARTIN A. NOWAK THE BELKNAP PRESS OF HARVARD UNIVERSITY PRESS CAMBRIDGE, MASSACHUSETTS, AND LONDON, ENGLAND 2006 Copyright © 2006 by the President and Fellows of Harvard College All rights reserved Printed in Canada Library of Congress Cataloging-in-Publication Data Nowak, M. A. (Martin A.) Evolutionary dynamics : exploring the equations of life / Martin A. Nowak. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-674-02338-3 (alk. paper) ISBN-10: 0-674-02338-2 (alk. paper) 1. Evolution (Biology)—Mathematical models. I. Title. QH371.3.M37N69 2006 576.8015118—dc22 2006042693 Designed by Gwen Nefsky Frankfeldt CONTENTS Preface ix 1 Introduction 1 2 What Evolution Is 9 3 Fitness Landscapes and Sequence Spaces 27 4 Evolutionary Games 45 5 Prisoners of the Dilemma 71 6 Finite Populations 93 7 Games in Finite Populations 107 8 Evolutionary Graph Theory 123 9 Spatial Games 145 10 HIV Infection 167 11 Evolution of Virulence 189 12 Evolutionary Dynamics of Cancer 209 13 Language Evolution 249 14 Conclusion 287 Further Reading 295 References 311 Index 349 PREFACE Evolutionary Dynamics presents those mathematical principles according to which life has evolved and continues to evolve. Since the 1950s biology, and with it the study of evolution, has grown enormously, driven by the quest to understand the world we live in and the stuff we are made of. Evolution is the one theory that transcends all of biology. Any observation of a living system must ultimately be interpreted in the context of its evolution. Because of the tremendous advances over the last half century, evolution has become a dis- cipline that is based on precise mathematical foundations. -
Modelo De Resumo Expandido
III Congresso de Iniciação Científica do INPA - CONIC Manaus, 14 a 18 de Julho de 2014. ISSN 2178 9665 ESTUDO DA MORFOLOGIA DA GENITÁLIA DAS ESPÉCIES DE Polistes (Aphanilopterus) DO NOVO MUNDO (HYMENOPTERA: VESPIDAE) Dark Gabriela Dolzane de CASTRO¹ Marcio Luiz de OLIVEIRA² Alexandre SOMAVILLA³ ¹Bolsista PIBIC/ CNPq; ² Orientador CBIO/INPA; ³Coorientador CBIO/INPA INTRODUÇÃO O estudo de vespas sociais, pertencentes à ordem Hymenoptera e conhecidas popularmente como vespas, marimbondos e cabas, permite identificar seu papel no equilíbrio trófico dos ecossistemas relacionado à sua duplicidade alimentar. Elas atuam como predadoras de larvas e insetos menores e também como coletoras de néctar e pólen (Carpenter e Marques 2001). Constituem um grupo com elevada riqueza de espécies e muito comum em áreas amazônicas. Trata-se aqui de Polistes, gênero na tribo Polistini, definido por Latreille (1802) tendo como espécie-tipo Vespa gallica Linnaeus, 1767. É um gênero cosmopolita com 204 espécies válidas, com ocorrência predominante nos trópicos (Richards 1978; Carpenter 1996a; Pickett et al. 2006). Para a região do Novo Mundo são registradas até o momento 93 espécies (Carpenter 1996a; Carpenter e Marques 2001). Morfologicamente, Polistes é caracterizado por vespas robustas, de tamanho geralmente grande (20 a 30 mm) quando comparado com outros vespídeos sociais, com exceção de Vespinae, tendo o primeiro segmento metassomal subséssil e uniformemente cônico em vista dorsal, o orifício do propódeo é agudo na parte dorsal e o pronoto possui uma carena posterior lateralmente à fóvea. A hipótese mais atual confirma o gênero como monofilético. Atualmente, apenas o subgênero Polistes (Aphanilopterus) abriga a fauna de Polistes da região Neotropical e Neártica. -
Gene Co-Expression Network Analysis Identifies Porcine Genes Associated
www.nature.com/scientificreports OPEN Gene co-expression network analysis identifies porcine genes associated with variation in Received: 16 September 2016 Accepted: 29 March 2017 metabolizing fenbendazole and Published: xx xx xxxx flunixin meglumine in the liver Jeremy T. Howard1, Melissa S. Ashwell1, Ronald E. Baynes2, James D. Brooks2, James L. Yeatts2 & Christian Maltecca1 Identifying individual genetic variation in drug metabolism pathways is of importance not only in livestock, but also in humans in order to provide the ultimate goal of giving the right drug at the right dose at the right time. Our objective was to identify individual genes and gene networks involved in metabolizing fenbendazole (FBZ) and flunixin meglumine (FLU) in swine liver. The population consisted of female and castrated male pigs that were sired by boars represented by 4 breeds. Progeny were randomly placed into groups: no drug (UNT), FLU or FBZ administered. Liver transcriptome profiles from 60 animals with extreme (i.e. fast or slow drug metabolism) pharmacokinetic (PK) profiles were generated from RNA sequencing. Multiple cytochrome P450 (CYP1A1, CYP2A19 and CYP2C36) genes displayed different transcript levels across treated versus UNT. Weighted gene co-expression network analysis identified 5 and 3 modules of genes correlated with PK parameters and a portion of these were enriched for biological processes relevant to drug metabolism for FBZ and FLU, respectively. Genes within identified modules were shown to have a higher transcript level relationship (i.e. connectivity) in treated versus UNT animals. Investigation into the identified genes would allow for greater insight into FBZ and FLU metabolism. Drug use and how it is currently regulated in livestock has received increased attention due to animal welfare concerns, food safety and the prevalence of antibiotic resistance1. -
Evolutionary Dynamics on Any Population Structure
Evolutionary dynamics on any population structure Benjamin Allen1,2,3, Gabor Lippner4, Yu-Ting Chen5, Babak Fotouhi1,6, Naghmeh Momeni1,7, Shing-Tung Yau3,8, and Martin A. Nowak1,8,9 1Program for Evolutionary Dynamics, Harvard University, Cambridge, MA, USA 2Department of Mathematics, Emmanuel College, Boston, MA, USA 3Center for Mathematical Sciences and Applications, Harvard University, Cambridge, MA, USA 4Department of Mathematics, Northeastern University, Boston, MA, USA 5Department of Mathematics, University of Tennessee, Knoxville, TN, USA 6Institute for Quantitative Social Sciences, Harvard University, Cambridge, MA, USA 7Department of Electrical and Computer Engineering, McGill University, Montreal, Canada 8Department of Mathematics, Harvard University, Cambridge, MA, USA 9Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA December 23, 2016 Abstract Evolution occurs in populations of reproducing individuals. The structure of a biological population affects which traits evolve [1, 2]. Understanding evolutionary game dynamics in structured populations is difficult. Precise results have been absent for a long time, but have recently emerged for special structures where all individuals have the arXiv:1605.06530v2 [q-bio.PE] 22 Dec 2016 same number of neighbors [3, 4, 5, 6, 7]. But the problem of deter- mining which trait is favored by selection in the natural case where the number of neighbors can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class which suggests there is no efficient algorithm [8]. Whether there exists a simple solution for weak selection was unanswered. Here we provide, surprisingly, a general formula for weak selection that applies to any graph or social network.