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1. A chromosome-level assembly of the black tiger shrimp ( Penaeus monodon ) genome facilitates the identification of novel growth-associated genes

Author

Wirulda Pootakham, Kanchana Sittikankaew, Frédéric Tangy, Piroon Jenjaroenpun, Jasper J. Koehorst, Intawat Nookaew, Tanaporn Uengwetwanit, Wanilada Rungrassamee, Peter J. Schaap, Pacharaporn Angthong, Jutatip Khudet, Sopacha Arayamethakorn, Rungnapa Leelatanawit, Duangjai Sangsrakru, Vitor A. P. Martins dos Santos, Thidathip Wongsurawat, Chutima Sonthirod, Nitsara Karoonuthaisiri, National Center for Genetic Engineering and Biotechnology [Thailand] (BIOTEC), National Science and Technology Development Agency [Bangkok] (NSTDA), University of Arkansas for Medical Sciences (UAMS), Shrimp Genetic Improvement Center [Thani, Thailand] (SGIC), National Science and Technology Development Agency [Bangkok] (NSTDA)-National Science and Technology Development Agency [Bangkok] (NSTDA), Department of Agrotechnology and Food Sciences [Wageningen], Wageningen University and Research [Wageningen] (WUR), Génomique virale et vaccination, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), This project was funded by National Center for Genetic Engineering and Biotechnology (BIOTEC Platform No. P1651718), TDG Food and Feed program, National Science and Technology Development Agency (No. P1950419) and partially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 734486 (SAFE-Aqua)., Visiting Professor Program 2019 was awarded to Dr. Intawat Nookaew by the National Science and Technology Development Agency, Thailand., The authors acknowledge NSTDA Supercomputer Center (ThaiSC) for providing HPC resources that have contributed to the research results reported within this paper. We thank Ms. Somjai Wongtripop, Mr. Kaidtisak kaewlok and Shrimp Genetic Improvement Center (SGIC) members, as well as Ms. Sudtida Phuengwas (BIOTEC) for the shrimp sample collection., We acknowledge Zulema Dominguez (University of Arkansas for Medical Sciences, USA) for technical assistance on comparative genome. We are grateful to Prof. Morakot Tanticharoen (NSTDA), Dr. Kanyawim Kirtikara (BIOTEC), Dr. Wonnop Visessanguan (BIOTEC) and Assoc Prof. Skorn Koonawootrittriron (Kasetsart University, Thailand) for their mentorship on the shrimp research programs., and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
growth-associated, [SDV]Life Sciences [q-bio], Sequence assembly, Computational biology, Genome, Penaeus monodon, 03 medical and health sciences, transcriptomics, Hi-C, 14. Life underwater, Black tiger shrimp, genes, Genome size, reference genome, 030304 developmental biology, 2. Zero hunger, Comparative genomics, Whole genome sequencing, PacBio, 0303 health sciences, biology, [SDV.BA]Life Sciences [q-bio]/Animal biology, 04 agricultural and veterinary sciences, Genome project, biology.organism_classification, 040102 fisheries, 0401 agriculture, forestry, and fisheries, Reference genome
Abstract

The black tiger shrimp (Penaeus monodon) is one of the most prominent farmed crustacean species with an average annual global production of 0.5 million tons in the last decade. To ensure sustainable and profitable production through genetic selective breeding programs, several research groups have attempted to generate a reference genome using short-read sequencing technology. However, the currently available assemblies lack the contiguity and completeness required for accurate genome annotation due to the highly repetitive nature of the genome and technical difficulty in extracting high-quality, high-molecular weight DNA in this species. Here, we report the first chromosome-level whole-genome assembly of P. monodon. The combination of long-read Pacific Biosciences (PacBio) and long-range Chicago and Hi-C technologies enabled a successful assembly of this first high-quality genome sequence. The final assembly covered 2.39 Gb (92.3% of the estimated genome size) and contained 44 pseudomolecules, corresponding to the haploid chromosome number. Repetitive elements occupied a substantial portion of the assembly (62.5%), highest of the figures reported among crustacean species. The availability of this high-quality genome assembly enabled the identification of novel genes associated with rapid growth in the black tiger shrimp through the comparison of hepatopancreas transcriptome of slow-growing and fast-growing shrimps. The results highlighted several gene groups involved in nutrient metabolism pathways and revealed 67 newly identified growth-associated genes. Our high-quality genome assembly provides an invaluable resource for accelerating the development of improved shrimp strain in breeding programs and future studies on gene regulations and comparative genomics.

Published
2020

2. Quantifying model evidence for yellow fever transmission routes in Africa

Author

Katy A. M. Gaythorpe, Laurence Cibrelus, Tini Garske, Kévin Jean, Imperial College London, Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers [CNAM] (CNAM), Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), World Health Organisation (WHO), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Funding:This work was carried out as part of the Vaccine Impact Modelling Consortium (www.vaccineimpact.org),but the views expressed are those of the authors and not necessarily those of the Consortium or its funders.The funders were given the opportunity to review this paper prior to publication,but the final decision on the content of the publication was taken by the authors.We acknowledge joint Centre funding from the UK Medical Research Council and Department for International Development. The research leading to these results has received funding from the Bill & Melinda Gates foundation (OPP1117543,OPP1157270 http://www.gatesfoundation.org/)and from the Medical Research Council (MR/R015600/1https://mrc.ukri.org/)., HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), and Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)

Subjects
0301 basic medicine, Viral Diseases, Epidemiology, Force of infection, Statics, Pathology and Laboratory Medicine, law.invention, Geographical Locations, Bayes' theorem, 0302 clinical medicine, law, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Aedes, Statistics, Medicine and Health Sciences, Public and Occupational Health, Biology (General), Ecology, Physics, Yellow fever, Linear model, Classical Mechanics, Vaccination and Immunization, Geography, Transmission (mechanics), Infectious Diseases, Serology, Computational Theory and Mathematics, INFECTIONS, Modeling and Simulation, Physical Sciences, Yellow fever virus, Life Sciences & Biomedicine, Research Article, Generalized linear model, Biochemistry & Molecular Biology, Bioinformatics, QH301-705.5, Immunology, Bayesian inference, Models, Biological, Biochemical Research Methods, BAYESIAN MODEL, Infectious Disease Epidemiology, 03 medical and health sciences, Cellular and Molecular Neuroscience, Yellow Fever, Genetics, medicine, Animals, Humans, Molecular Biology, 01 Mathematical Sciences, Ecology, Evolution, Behavior and Systematics, Estimation, Science & Technology, Models, Statistical, Biology and Life Sciences, Computational Biology, Bayes Theorem, 06 Biological Sciences, medicine.disease, 030104 developmental biology, People and Places, Africa, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Mathematical & Computational Biology, 08 Information and Computing Sciences, Preventive Medicine, 030217 neurology & neurosurgery
Abstract

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data., Author summary Yellow fever (YF) is notoriously under reported due to non-specific symptom spectrum and the true burden is highly uncertain as a result of a complex transmission cycle. As such, estimates surrounding YF burden are highly uncertain and the mechanisms behind transmission are often unclear. We assess these mechanisms and the resulting uncertainty by estimating two existing models of YF transmission within a product space framework. This allows us to produce updated estimates of transmission intensity and to compare the relative support for each model given the data. We find strong support for a model assuming a static force of infection, approximating the constant infection pressure from the sylvatic reservoir of YF. We also highlight areas where data is sparse, often the same areas estimated to have especially high transmission intensity. This is the first robust multi-model approach to applied YF modelling and provides a framework that could be extended to other disease models.

Published
2019

3. Taxonomy of prokaryotic viruses: 2017 update from the ICTV Bacterial and Archaeal Viruses Subcommittee

Author

Adriaenssens, Evelien M, Wittmann, Johannes, Kuhn, Jens H, Turner, Dann, Sullivan, Matthew B., Dutilh, Bas E, Jang, Ho Bin, van Zyl, Leonardo J, Klumpp, Jochen, Lobocka, Malgorzata, Moreno Switt, Andrea I, Rumnieks, Janis, Edwards, Robert A, Uchiyama, Jumpei, Alfenas-Zerbini, Poliane, Petty, Nicola K, Kropinski, Andrew M, Barylski, Jakub, Gillis, Annika, Clokie, Martha R C, Prangishvili, David, Lavigne, Rob, Aziz, Ramy Karam, Duffy, Siobain, Krupovic, Mart, Poranen, Minna M., Knezevic, Petar, Enault, Francois, Tong, Yigang, Oksanen, Hanna M., Rodney Brister, J, Sub Bioinformatics, Theoretical Biology and Bioinformatics, University of Liverpool, Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH / Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (DSMZ), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), National Institutes of Health [Bethesda] (NIH), University of the West of England [Bristol] (UWE Bristol), Ohio State University [Columbus] (OSU), Utrecht University [Utrecht], Radboud University Medical Center [Nijmegen], University of the Western Cape, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Polish Academy of Sciences (PAN), Universidad Andrés Bello - UNAB (CHILE), Latvian Biomedical Research and Study Centre [Rīga], San Diego State University (SDSU), Azabu University, Universidade Federal de Vicosa (UFV), iThree Institute, University of Technology Sydney (UTS), University of Guelph, Adam Mickiewicz University in Poznań (UAM), Université Catholique de Louvain = Catholic University of Louvain (UCL), University of Leicester, Biologie Moléculaire du Gène chez les Extrêmophiles (BMGE), Institut Pasteur [Paris], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Cairo University, University of Warwick [Coventry], University of Helsinki, University of Novi Sad, Laboratoire Microorganismes : Génome et Environnement (LMGE), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Beijing Institute of Microbiology and Epidemiology, This work was funded in part through Battelle Memorial Institute’s prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272200700016I (J.H.K.). B.E.D. was supported by the Netherlands Organization for Scientific Research (NWO), Vidi Grant 864.14.004. R.A.E was supported by grant MCB-1330800 from the National Science Foundation. J.R.B. was supported by the Intramural Research Program of the National Institutes of Health, National Library of Medicine. R.L. is a member of the phagebiotics research community, supported by FWO Vlaanderen. M.M.P. was supported by the Academy of Finland (272507). A.G. was supported by the National Fund for Scientific Research (FNRS). H.M.O. was supported by University of Helsinki funding for Instruct-F1 research infrastructure., The committee would like to thank Dr. Graham Hatfull (University of Pittsburgh) for permitting us to use Actinobacteriophage Database electron micrographs in 2017’s taxonomy proposals. The authors thank Laura Bollinger (National Institutes of Health/National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, MD, USA) for editing this paper., University of the Western Cape (UWC), Universidad Andrés Bello [Santiago] (UNAB), Universidade Federal de Viçosa = Federal University of Viçosa (UFV), Institut Pasteur [Paris] (IP), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), UCL - SST/ELI/ELIM - Applied Microbiology, Sub Bioinformatics, and Theoretical Biology and Bioinformatics

Subjects
0301 basic medicine, Archaeal Viruses, viruses, [SDV]Life Sciences [q-bio], 030106 microbiology, education, Coronacrisis-Taverne, Zoology, Computational biology, Biology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 03 medical and health sciences, Phylogenetics, Virology, MESH: Archaeal Viruses, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], Bacteriophages, MESH: Bacteriophages, Virus classification, Taxonomy, General Medicine, 030104 developmental biology, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, ICTV bacterial, Classification methods, Taxonomy (biology), Bacterial virus
Abstract

International audience; The prokaryotic virus community is represented at the International Committee on Taxonomy of Viruses (ICTV) by the Bacterial and Archaeal Viruses Subcommittee. Since our last report [5], the committee composition has changed, and a large number of taxonomic proposals (TaxoProps) were submitted to the ICTV Executive Committee (EC) for approval.

Published
2018