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      Divergent evolutionary trajectories following speciation in two ectoparasitic honey bee mites

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          Abstract

          Multispecies host-parasite evolution is common, but how parasites evolve after speciating remains poorly understood. Shared evolutionary history and physiology may propel species along similar evolutionary trajectories whereas pursuing different strategies can reduce competition. We test these scenarios in the economically important association between honey bees and ectoparasitic mites by sequencing the genomes of the sister mite species Varroa destructor and Varroa jacobsoni. These genomes were closely related, with 99.7% sequence identity. Among the 9,628 orthologous genes, 4.8% showed signs of positive selection in at least one species. Divergent selective trajectories were discovered in conserved chemosensory gene families (IGR, SNMP), and Halloween genes (CYP) involved in moulting and reproduction. However, there was little overlap in these gene sets and associated GO terms, indicating different selective regimes operating on each of the parasites. Based on our findings, we suggest that species-specific strategies may be needed to combat evolving parasite communities.

          Abstract

          Maeva Techer et al. report genome assemblies of two honeybee parasitic mites, Varroa destructor and V. jacobsoni. They find that 4.8% of orthologous genes show evidence of positive selection in at least one species, though the genes under selection are distinct between species indicating divergent evolution.

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          Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets

          High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets ( 150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.
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            Revisiting the insect mitochondrial molecular clock: the mid-Aegean trench calibration.

            Phylogenetic trees in insects are frequently dated by applying a "standard" mitochondrial DNA (mtDNA) clock estimated at 2.3% My(-1), but despite its wide use reliable calibration points have been lacking. Here, we used a well-established biogeographic barrier, the mid-Aegean trench separating the western and eastern Aegean archipelago, to estimate substitution rates in tenebrionid beetles. Cytochrome oxidase I (cox1) for six codistributed genera across 28 islands (444 individuals) on both sides of the mid-Aegean trench revealed 60 independently coalescing entities delimited with a mixed Yule-coalescent model. One representative per entity was used for phylogenetic analysis of mitochondrial (cox1, 16S rRNA) and nuclear (Mp20, 28S rRNA) genes. Six nodes marked geographically congruent east-west splits whose separation was largely contemporaneous and likely to reflect the formation of the mid-Aegean trench at 9-12 Mya. Based on these "known" dates, a divergence rate of 3.54% My(-1) for the cox1 gene (2.69% when combined with the 16S rRNA gene) was obtained under the preferred partitioning scheme and substitution model selected using Bayes factors. An extensive survey suggests that discrepancies in mtDNA substitution rates in the entomological literature can be attributed to the use of different substitution models, the use of different mitochondrial gene regions, mixing of intraspecific with interspecific data, and not accounting for variance in coalescent times or postseparation gene flow. Different treatments of these factors in the literature confound estimates of mtDNA substitution rates in opposing directions and obscure lineage-specific differences in rates when comparing data from various sources.
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              Varroamites and honey bee health: canVarroaexplain part of the colony losses?

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                Author and article information

                Contributors
                maeva.techer@oist.jp
                alexander.mikheyev@oist.jp
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                1 October 2019
                1 October 2019
                2019
                : 2
                : 357
                Affiliations
                [1 ]ISNI 0000 0000 9805 2626, GRID grid.250464.1, Okinawa Institute of Science and Technology, ; 1919-1 Tancha Onna-son, 904-0495 Okinawa, Japan
                [2 ]GRID grid.1016.6, Commonwealth Scientific and Industrial Research Organisation, ; Clunies Ross St, (GPO Box 1700), Acton, ACT 2601 Australia
                [3 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Bio21 Institute, School of BioSciences, , University of Melbourne, ; 30 Flemington Road, Parkville, VIC 3010 Australia
                [4 ]Phase Genomics Inc, Seattle, WA 98195 USA
                [5 ]USDA-ARS Bee Research Lab, Beltsville, MD USA
                [6 ]ISNI 0000 0001 2180 7477, GRID grid.1001.0, Australian National University, ; Canberra, ACT 2600 Australia
                Author information
                http://orcid.org/0000-0001-5417-5103
                http://orcid.org/0000-0001-9739-5595
                http://orcid.org/0000-0002-0747-8539
                http://orcid.org/0000-0002-0036-4651
                http://orcid.org/0000-0003-4369-1019
                Article
                606
                10.1038/s42003-019-0606-0
                6773775
                31583288
                0a02d5e7-a929-465a-a8d5-f9a847d266ad
                © 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/.

                History
                : 20 January 2019
                : 10 September 2019
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                Custom metadata
                © The Author(s) 2018

                coevolution,comparative genomics
                coevolution, comparative genomics

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