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Genome-Wide Association Analyses Point to Candidate Genes for Electric Shock Avoidance in Drosophila melanogaster

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      Abstract

      Electric shock is a common stimulus for nociception-research and the most widely used reinforcement in aversive associative learning experiments. Yet, nothing is known about the mechanisms it recruits at the periphery. To help fill this gap, we undertook a genome-wide association analysis using 38 inbred Drosophila melanogaster strains, which avoided shock to varying extents. We identified 514 genes whose expression levels and/ or sequences co-varied with shock avoidance scores. We independently scrutinized 14 of these genes using mutants, validating the effect of 7 of them on shock avoidance. This emphasizes the value of our candidate gene list as a guide for follow-up research. In addition, by integrating our association results with external protein-protein interaction data we obtained a shock avoidance-associated network of 38 genes. Both this network and the original candidate list contained a substantial number of genes that affect mechanosensory bristles, which are hair-like organs distributed across the fly’s body. These results may point to a potential role for mechanosensory bristles in shock sensation. Thus, we not only provide a first list of candidate genes for shock avoidance, but also point to an interesting new hypothesis on nociceptive mechanisms.

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      Most cited references 34

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      Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

      DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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        Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

        Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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          affy--analysis of Affymetrix GeneChip data at the probe level.

          The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.
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            Author and article information

            Affiliations
            [1 ]Research Group Molecular Systems Biology of Learning, Leibniz Institute of Neurobiology, Magdeburg, Germany
            [2 ]Max Planck Institute of Neurobiology, Martinsried, Germany
            [3 ]Laboratory for Microarray Applications, IZKF, University of Würzburg, Würzburg, Germany
            [4 ]Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
            [5 ]Institute of Human Genetics, University of Würzburg, Würzburg, Germany
            [6 ]Department of Physiological Chemistry, Butenandt Institute and LMU Biomedical Center, Faculty of Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
            [7 ]Tohoku University Graduate School of Life Sciences, Sendai, Japan
            [8 ]Center for Behavioral Brain Sciences, Magdeburg, Germany
            Alexander Fleming Biomedical Sciences Research Center, GREECE
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: MA C-JS TM MD CK TS CM HT AY. Performed the experiments: MA CK MB TO AK EA TS AY. Analyzed the data: MA C-JS TM MD CK TS AY. Contributed reagents/materials/analysis tools: C-JS TM MD CM HT. Wrote the paper: MA C-JS TM MD AY.

            Contributors
            Role: Academic Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            18 May 2015
            2015
            : 10
            : 5
            25992709 4436303 10.1371/journal.pone.0126986 PONE-D-14-44326

            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

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            Figures: 6, Tables: 0, Pages: 18
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            Funding
            This study was funded by a research grant from the Deutche Forschungsgemeinschaft to AY (YA272/2-1) ( http://www.dfg.de/) and institutional support from the Max Planck Institute for Neurobiology ( http://www.neuro.mpg.de/) and the Leibniz Institute for Neurobiology ( http://www.lin-magdeburg.de/index.jsp). MA received a PhD stipend from Studienstiftung des deutschen Volkes ( http://www.studienstiftung.de/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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            All relevant data are within the paper and its Supporting Information files.

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