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      Structure and proteomic analysis of the crown-of-thorns starfish ( Acanthaster sp.) radial nerve cord

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          Abstract

          The nervous system of the Asteroidea (starfish or seastar) consists of radial nerve cords (RNCs) that interconnect with a ring nerve. Despite its relative simplicity, it facilitates the movement of multiple arms and numerous tube feet, as well as regeneration of damaged limbs. Here, we investigated the RNC ultrastructure and its molecular components within the of Pacific crown-of-thorns starfish (COTS; Acanthaster sp.), a well-known coral predator that in high-density outbreaks has major ecological impacts on coral reefs. We describe the presence of an array of unique small bulbous bulbs (40–100 μm diameter) that project from the ectoneural region of the adult RNC. Each comprise large secretory-like cells and prominent cilia. In contrast, juvenile COTS and its congener Acanthaster brevispinus lack these features, both of which are non-corallivorous. Proteomic analysis of the RNC (and isolated neural bulbs) provides the first comprehensive echinoderm protein database for neural tissue, including numerous secreted proteins associated with signalling, transport and defence. The neural bulbs contained several neuropeptides (e.g., bombyxin-type, starfish myorelaxant peptide, secretogranin 7B2-like, Ap15a-like, and ApNp35) and Deleted in Malignant Brain Tumor 1-like proteins. In summary, this study provides a new insight into the novel traits of COTS, a major pest on coral reefs, and a proteomics resource that can be used to develop (bio)control strategies and understand molecular mechanisms of regeneration.

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          Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

          We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.
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            ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

            The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.
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              SignalP 4.0: discriminating signal peptides from transmembrane regions.

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                Author and article information

                Contributors
                scummins@usc.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 February 2023
                27 February 2023
                2023
                : 13
                : 3349
                Affiliations
                [1 ]GRID grid.1034.6, ISNI 0000 0001 1555 3415, Centre for Bioinnovation, , University of the Sunshine Coast, ; Maroochydore DC, QLD 4558 Australia
                [2 ]GRID grid.1034.6, ISNI 0000 0001 1555 3415, School of Science, Technology and Engineering, , University of the Sunshine Coast, ; Maroochydore DC, QLD 4558 Australia
                [3 ]GRID grid.250464.1, ISNI 0000 0000 9805 2626, Okinawa Institute of Science and Technology, ; Okinawa, Japan
                [4 ]GRID grid.4868.2, ISNI 0000 0001 2171 1133, School of Biological and Chemical Sciences, , Queen Mary University of London, ; London, UK
                [5 ]GRID grid.1046.3, ISNI 0000 0001 0328 1619, Australian Institute of Marine Science (AIMS), ; Cape Ferguson, Townsville, QLD 4810 Australia
                [6 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, School of Life and Environmental Sciences, , University of Sydney, ; Sydney, NSW 2006 Australia
                Article
                30425
                10.1038/s41598-023-30425-1
                9971248
                36849815
                75555694-20e7-4238-967a-62da3e4f42b9
                © The Author(s) 2023

                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
                : 27 September 2022
                : 22 February 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/M001644/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000275, Leverhulme Trust;
                Award ID: RPG-2013-351
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                gene expression analysis,proteomic analysis,molecular neuroscience,neurophysiology

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