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      Potato leafroll virus reduces Buchnera aphidocola titer and alters vector transcriptome responses

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          Abstract

          Viruses in the Luteoviridae family, such as Potato leafroll virus (PLRV), are transmitted by aphids in a circulative and nonpropagative mode. This means the virions enter the aphid body through the gut when they feed from infected plants and then the virions circulate through the hemolymph to enter the salivary glands before being released into the saliva. Although these viruses do not replicate in their insect vectors, previous studies have demonstrated viruliferous aphid behavior is altered and the obligate symbiont of aphids, Buchnera aphidocola, may be involved in transmission. Here we provide the transcriptome of green peach aphids ( Myzus persicae) carrying PLRV and virus-free control aphids using Illumina sequencing. Over 150 million paired-end reads were obtained through Illumina sequencing, with an average of 19 million reads per library. The comparative analysis identified 134 differentially expressed genes (DEGs) between the M. persicae transcriptomes, including 64 and 70 genes that were up- and down-regulated in aphids carrying PLRV, respectively. Using functional classification in the GO databases, 80 of the DEGs were assigned to 391 functional subcategories at category level 2. The most highly up-regulated genes in aphids carrying PLRV were cytochrome p450s, genes related to cuticle production, and genes related to development, while genes related to heat shock proteins, histones, and histone modification were the most down-regulated. PLRV aphids had reduced Buchnera titer and lower abundance of several Buchnera transcripts related to stress responses and metabolism. These results suggest carrying PLRV may reduce both aphid and Buchnera genes in response to stress. This work provides valuable basis for further investigation into the complicated mechanisms of circulative and nonpropagative transmission.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            HTSeq—a Python framework to work with high-throughput sequencing data

            Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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                Author and article information

                Contributors
                ccasteel@cornell.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 December 2021
                14 December 2021
                2021
                : 11
                : 23931
                Affiliations
                [1 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Department of Plant Pathology, , University of California, ; Davis, CA 95616 USA
                [2 ]GRID grid.5386.8, ISNI 000000041936877X, Plant Pathology and Plant-Microbe Biology Section, School of Integrated Plant Science, , Cornell University, ; Ithaca, NY 14850 USA
                [3 ]GRID grid.266097.c, ISNI 0000 0001 2222 1582, Department of Entomology, , University of California, ; Riverside, CA 92521 USA
                Article
                2673
                10.1038/s41598-021-02673-6
                8671517
                34907187
                904f5f62-0d07-42a0-9b3c-18acafb4ad79
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 May 2021
                : 9 November 2021
                Funding
                Funded by: USDA-NIFA 2017-67013-26537 and 2013-2013-03265
                Funded by: NSF 1723926
                Categories
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                © The Author(s) 2021

                Uncategorized
                ecology,molecular ecology
                Uncategorized
                ecology, molecular ecology

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