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      Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae

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          Abstract

          Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.

          Abstract

          Predicting how pathogen populations will change over time is challenging. This study predicts population changes in human pathogen Streptococcus pneumoniae after vaccination, using a model of negative frequency-dependent selection acting on accessory gene products.

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            APE: Analyses of Phylogenetics and Evolution in R language.

            Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. The program is free and available from the official R package archive at http://cran.r-project.org/src/contrib/PACKAGES.html#ape. APE is licensed under the GNU General Public License.
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              The microbial pan-genome.

              A decade after the beginning of the genomic era, the question of how genomics can describe a bacterial species has not been fully addressed. Experimental data have shown that in some species new genes are discovered even after sequencing the genomes of several strains. Mathematical modeling predicts that new genes will be discovered even after sequencing hundreds of genomes per species. Therefore, a bacterial species can be described by its pan-genome, which is composed of a "core genome" containing genes present in all strains, and a "dispensable genome" containing genes present in two or more strains and genes unique to single strains. Given that the number of unique genes is vast, the pan-genome of a bacterial species might be orders of magnitude larger than any single genome.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ValidationRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                22 October 2020
                October 2020
                22 October 2020
                : 18
                : 10
                : e3000878
                Affiliations
                [1 ] Burnett School of Biomedical Sciences, University of Central Florida, Orlando, Florida, United States of America
                [2 ] Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
                [3 ] Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [4 ] Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
                [5 ] Department of Biostatistics, University of Oslo, Oslo, Norway
                [6 ] Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
                [7 ] Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
                [8 ] MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
                [9 ] World Health Organization, Geneva, Switzerland
                [10 ] Department of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
                Wageningen University, NETHERLANDS
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests. ML has consulted for Pfizer, Affinivax, and Merck and has received grant support not related to this paper from Pfizer and PATH Vaccine Solutions. WPH, ML, and NJC have consulted for Antigen Discovery Inc. The authors have declared that no competing interests exist. KLOB has received grant support for pneumococcal work not related to this paper from Pfizer, GSK, and Gavi. KLOB has consulted for Merck and Sanofi Pasteur. LRG, LLH, and RCW have received grant support not related to this paper from Pfizer, Merck, and GSK.

                [¤a]

                Current address: Department of Microbiology, University of Illinois at Urbana Champaign, Illinois, United States of America.

                ‡ TA and PPM are joint first authors on this work. ML and WPH are joint senior authors on this work.

                Author information
                https://orcid.org/0000-0002-8611-5241
                https://orcid.org/0000-0002-3607-0938
                https://orcid.org/0000-0001-6810-7304
                https://orcid.org/0000-0002-7752-1942
                https://orcid.org/0000-0003-2399-9657
                https://orcid.org/0000-0001-6303-8768
                https://orcid.org/0000-0001-6051-1484
                https://orcid.org/0000-0003-1504-9213
                https://orcid.org/0000-0002-6319-7336
                Article
                PBIOLOGY-D-20-00582
                10.1371/journal.pbio.3000878
                7580979
                33091022
                3e4d6e7b-f9e5-412f-abea-f8d576b7efcb
                © 2020 Azarian et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 March 2020
                : 18 September 2020
                Page count
                Figures: 3, Tables: 1, Pages: 20
                Funding
                Funded by: National Institutes of Health (US)
                Award ID: R01 AI106786
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 AI048935
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 104169/Z/14/A
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 742158
                Award Recipient :
                TA and WPH were funded by NIH grant R01 AI106786. TA, PPM, and ML were funded by NIH grant R01 AI048935. XQ was supported by the National Institute of General Medical Sciences of NIH under Award Number U54GM088558. NJC was funded by a Sir Henry Dale fellowship and jointly funded by the Wellcome Trust and Royal Society (Grant Number 104169/Z/14/A). JC was funded by European Research Council grant number 742158. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Genomics
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Biology and Life Sciences
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                Medicine and Health Sciences
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                Custom metadata
                Whole-genome sequencing data are available from NCBI under BioProject PRJEB8327: https://www.ncbi.nlm.nih.gov/bioproject/PRJEB8327. Accession numbers and accompanying metadata have previously been published. All R code and associated input data frames as well as phylogenies are provided in the supporting information and are available on GitHub here: https://github.com/c2-d2/Projects/tree/master/NFDS.

                Life sciences
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