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      Analysis of sea star larval regeneration reveals conserved processes of whole-body regeneration across the metazoa

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          Abstract

          Background

          Metazoan lineages exhibit a wide range of regenerative capabilities that vary among developmental stage and tissue type. The most robust regenerative abilities are apparent in the phyla Cnidaria, Platyhelminthes, and Echinodermata, whose members are capable of whole-body regeneration (WBR). This phenomenon has been well characterized in planarian and hydra models, but the molecular mechanisms of WBR are less established within echinoderms, or any other deuterostome system. Thus, it is not clear to what degree aspects of this regenerative ability are shared among metazoa.

          Results

          We characterize regeneration in the larval stage of the Bat Star ( Patiria miniata). Following bisection along the anterior-posterior axis, larvae progress through phases of wound healing and re-proportioning of larval tissues. The overall number of proliferating cells is reduced following bisection, and we find evidence for a re-deployment of genes with known roles in embryonic axial patterning. Following axial respecification, we observe a significant localization of proliferating cells to the wound region. Analyses of transcriptome data highlight the molecular signatures of functions that are common to regeneration, including specific signaling pathways and cell cycle controls. Notably, we find evidence for temporal similarities among orthologous genes involved in regeneration from published Platyhelminth and Cnidarian regeneration datasets.

          Conclusions

          These analyses show that sea star larval regeneration includes phases of wound response, axis respecification, and wound-proximal proliferation. Commonalities of the overall process of regeneration, as well as gene usage between this deuterostome and other species with divergent evolutionary origins reveal a deep similarity of whole-body regeneration among the metazoa.

          Electronic supplementary material

          The online version of this article (10.1186/s12915-019-0633-9) contains supplementary material, which is available to authorized users.

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          Most cited references88

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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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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                Author and article information

                Contributors
                gregcary@andrew.cmu.edu
                awolff@andrew.cmu.edu
                ozueva@andrew.cmu.edu
                jpattina@andrew.cmu.edu
                412 268 9348 , veronica@cmu.edu
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                22 February 2019
                22 February 2019
                2019
                : 17
                : 16
                Affiliations
                ISNI 0000 0001 2097 0344, GRID grid.147455.6, Department of Biological Sciences, , Carnegie Mellon University, Mellon Institute, ; 4400 Fifth Ave, Pittsburgh, PA 15213 USA
                Author information
                http://orcid.org/0000-0003-3414-1357
                Article
                633
                10.1186/s12915-019-0633-9
                6385403
                30795750
                04cab22f-b083-4595-84b4-7ef7f8d5a5a3
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 November 2018
                : 4 February 2019
                Funding
                Funded by: Charles Kaufman Award from the Pittsburgh Foundation
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Life sciences
                regeneration,sea star,echinoderm,transcriptome
                Life sciences
                regeneration, sea star, echinoderm, transcriptome

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