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      Integrative taxonomy confirms that Gregarina garnhami and G. acridiorum (Apicomplexa, Gregarinidae), parasites of Schistocerca gregaria and Locusta migratoria (Insecta, Orthoptera), are distinct species Translated title: La taxonomie intégrative confirme que Gregarina garnhami et G. acridiorum (Apicomplexa, Gregarinidae), parasites de Schistocerca gregaria et Locusta migratoria (Insecta, Orthoptera), sont des espèces distinctes

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          Abstract

          Orthoptera are infected by about 60 species of gregarines assigned to the genus Gregarina Dufour, 1828. Among these species, Gregarina garnhami Canning, 1956 from Schistocerca gregaria (Forsskål, 1775) was considered by Lipa et al. in 1996 to be synonymous with Gregarina acridiorum (Léger 1893), a parasite of several orthopteran species including Locusta migratoria (Linné, 1758). Here, a morphological study and molecular analyses of the SSU rDNA marker demonstrate that specimens of S. gregaria and specimens of L. migratoria are infected by two distinct Gregarina species, G. garnhami and G. acridiorum, respectively. Validation of the species confirms that molecular analyses provide useful taxonomical information. Phenotypic plasticity was clearly observed in the case of G. garnhami: the morphology of its trophozoites, gamonts and syzygies varied according to the geographical location of S. gregaria and the subspecies infected.

          Translated abstract

          Les orthoptères sont parasités par environ soixante espèces de grégarines affiliées au genre Gregarina Dufour, 1828. Parmi ces espèces Gregarina garnhami Canning, 1956 décrite chez Schistocerca gregaria (Forskål, 1775), a été mise en synonymie par Lipa et al. en 1996 avec Gregarina acridiorum (Léger 1893), parasite de plusieurs espèces d’orthoptères dont Locusta migratoria (Linné, 1758). Ici, une étude morphologique et des analyses moléculaires du marqueur SSU rDNA démontrent que les spécimens de S. gregaria et ceux de L. migratoria sont infectés par 2 espèces distinctes de grégarines, Gregarina garnhami et Gregarina acridiorum, respectivement. La validation de ces espèces confirme l’importance des informations fournies par les analyses moléculaires dans les études taxonomiques. Une plasticité phénotypique a été clairement observée dans le cas de G. garnhami : la morphologie de ses trophozoïtes, gamontes et syzygies varie selon la localisation géographique et la sous-espèce de S. gregaria infectée.

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          Most cited references 43

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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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            MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

            The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Journal
                Parasite
                Parasite
                parasite
                Parasite
                EDP Sciences
                1252-607X
                1776-1042
                2021
                23 February 2021
                : 28
                : ( publisher-idID: parasite/2021/01 )
                Affiliations
                [1 ] Molécules de Communication et Adaptation des Microorganismes (MCAM, UMR 7245 CNRS), Département Adaptations du vivant (AVIV), Muséum National d’Histoire Naturelle, CNRS CP 52 57 rue Cuvier 75231 Paris Cedex 05 France
                [2 ] CBGP, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD 34060 Montpellier France
                [3 ] CIRAD, UMR CBGP 34398 Montpellier France
                [4 ] Structure et instabilité des génomes (STRING UMR 7196 CNRS/INSERM U1154), Département Adaptations du vivant (AVIV), Muséum National d’Histoire Naturelle, CNRS, INSERM CP 26 57 rue Cuvier 75231 Paris Cedex 05 France
                Author notes
                [* ]Corresponding author: isabelle.florent@ 123456mnhn.fr
                Article
                parasite200113 10.1051/parasite/2021009
                10.1051/parasite/2021009
                7901526
                © I. Florent et al., published by EDP Sciences, 2021

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 3, Tables: 3, Equations: 0, References: 42, Pages: 15
                Categories
                Research Article

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