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      Dual role of the Anopheles coluzzii Venus Kinase Receptor in both larval growth and immunity

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          Abstract

          Vector-borne diseases and especially malaria are responsible for more than half million deaths annually. The increase of insecticide resistance in wild populations of Anopheles malaria vectors emphasises the need for novel vector control strategies as well as for identifying novel vector targets. Venus kinase receptors (VKRs) constitute a Receptor Tyrosine Kinase (RTK) family only found in invertebrates. In this study we functionally characterized Anopheles VKR in the Gambiae complex member, Anopheles coluzzii. Results showed that Anopheles VKR can be activated by L-amino acids, with L-arginine as the most potent agonist. VKR was not required for the fecundity of A. coluzzii, in contrast to reports from other insects, but VKR function is required in both Anopheles males and females for development of larval progeny. Anopheles VKR function is also required for protection against infection by Plasmodium parasites, thus identifying a novel linkage between reproduction and immunity in Anopheles. The insect specificity of VKRs as well as the essential function for reproduction and immunity suggest that Anopheles VKR could be a potentially druggable target for novel vector control strategies.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015

            Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015 and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.
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              The host defense of Drosophila melanogaster.

              To combat infection, the fruit fly Drosophila melanogaster relies on multiple innate defense reactions, many of which are shared with higher organisms. These reactions include the use of physical barriers together with local and systemic immune responses. First, epithelia, such as those beneath the cuticle, in the alimentary tract, and in tracheae, act both as a physical barrier and local defense against pathogens by producing antimicrobial peptides and reactive oxygen species. Second, specialized hemocytes participate in phagocytosis and encapsulation of foreign intruders in the hemolymph. Finally, the fat body, a functional equivalent of the mammalian liver, produces humoral response molecules including antimicrobial peptides. Here we review our current knowledge of the molecular mechanisms underlying Drosophila defense reactions together with strategies evolved by pathogens to evade them.
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                Author and article information

                Contributors
                colette.dissous@pasteur-lille.fr
                christian.mitri@pasteur.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 March 2019
                5 March 2019
                2019
                : 9
                Affiliations
                [1 ]ISNI 0000 0001 2159 9858, GRID grid.8970.6, CIIL- Institut Biologie de Lille, Inserm U1019, CNRS UMR 8204, Institut Pasteur Lille, ; Lille, France
                [2 ]ISNI 0000 0001 2353 6535, GRID grid.428999.7, Genetics and Genomics of Insect Vectors Unit, Department of Parasites and Insect Vectors, Institut Pasteur, ; Paris, France
                [3 ]ISNI 0000 0001 2112 9282, GRID grid.4444.0, Centre National de la Recherche Scientifique, ; UMR2000 Paris, France
                [4 ]Present Address: Oncogenesis of Lymphoma unit, INSERM U1053 - BaRITOn, Bordeaux, France
                [5 ]ISNI 0000 0004 1936 8753, GRID grid.137628.9, Present Address: Department of Basic Science & Craniofacial Biology, , New York University, College of Dentistry, ; New York, USA
                [6 ]ISNI 0000 0001 2353 6535, GRID grid.428999.7, Institut Pasteur – Bioinformatics and Biostatistics Hub – C3BI, USR, ; 3756 IP CNRS Paris, France
                [7 ]ISNI 0000 0001 2242 6780, GRID grid.503422.2, Team “Signal Division Regulation”, CNRS UMR 8576, University of Lille, ; Lille, France
                Article
                40407
                10.1038/s41598-019-40407-x
                6401105
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: European Commission, Horizon 2020 Infrastructures 731060 Infravec2; European Research Council, Support for frontier research, Advanced Grant #323173; Agence Nationale de la Recherche Laboratoire d'Excellence ‘Integrative Biology of Emerging Infectious Diseases’ #ANR-10-LABX-62-IBEID.
                Funded by: CNRS
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