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      Molecular characterization of a marine turtle tumor epizootic, profiling external, internal and postsurgical regrowth tumors

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          Abstract

          Sea turtle populations are under threat from an epizootic tumor disease (animal epidemic) known as fibropapillomatosis. Fibropapillomatosis continues to spread geographically, with prevalence of the disease also growing at many longer-affected sites globally. However, we do not yet understand the precise environmental, mutational and viral events driving fibropapillomatosis tumor formation and progression.

          Here we perform transcriptomic and immunohistochemical profiling of five fibropapillomatosis tumor types: external new, established and postsurgical regrowth tumors, and internal lung and kidney tumors. We reveal that internal tumors are molecularly distinct from the more common external tumors. However, they have a small number of conserved potentially therapeutically targetable molecular vulnerabilities in common, such as the MAPK, Wnt, TGFβ and TNF oncogenic signaling pathways. These conserved oncogenic drivers recapitulate remarkably well the core pan-cancer drivers responsible for human cancers. Fibropapillomatosis has been considered benign, but metastatic-related transcriptional signatures are strongly activated in kidney and established external tumors. Tumors in turtles with poor outcomes (died/euthanized) have genes associated with apoptosis and immune function suppressed, with these genes providing putative predictive biomarkers.

          Together, these results offer an improved understanding of fibropapillomatosis tumorigenesis and provide insights into the origins, inter-tumor relationships, and therapeutic treatment for this wildlife epizootic.

          Abstract

          Yetsko, Farrell, Duffy, and colleagues conduct transcriptomic and immunohistological profiling of tumors from sea turtles with fibropapillomatosis. Internal tumors are distinct from more common external tumors, but share some oncogenic signaling pathways that may serve as treatment targets in future.

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          Most cited references74

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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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
                duffy@whitney.ufl.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                1 February 2021
                1 February 2021
                2021
                : 4
                : 152
                Affiliations
                [1 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, The Whitney Laboratory for Marine Bioscience and Sea Turtle Hospital, , University of Florida, ; St. Augustine, FL 32080 USA
                [2 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Department of Biology, , University of Florida, ; Gainesville, FL 32611 USA
                [3 ]GRID grid.449717.8, ISNI 0000 0004 5374 269X, Department of Human Genetics, School of Medicine, , University of Texas Rio Grande Valley, ; Brownsville, TX USA
                [4 ]GRID grid.449717.8, ISNI 0000 0004 5374 269X, South Texas Diabetes and Obesity Institute, School of Medicine, , University of Texas Rio Grande Valley, ; Brownsville, TX USA
                [5 ]GRID grid.10049.3c, ISNI 0000 0004 1936 9692, Department of Biological Sciences, School of Natural Sciences, Faculty of Science and Engineering, , University of Limerick, ; Limerick, Ireland
                [6 ]GRID grid.5335.0, ISNI 0000000121885934, Transmissible Cancer Group, Department of Veterinary Medicine, , University of Cambridge, ; Cambridge, CB3 0ES UK
                [7 ]GRID grid.7886.1, ISNI 0000 0001 0768 2743, Systems Biology Ireland & Precision Oncology Ireland, School of Medicine, , University College Dublin, ; Belfield, Dublin, 4 Ireland
                [8 ]GRID grid.7362.0, ISNI 0000000118820937, Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, , Bangor University, ; Bangor, Gwynedd, LL57 2UW UK
                [9 ]Sea Turtle Inc., South Padre Island, TX USA
                [10 ]Gladys Porter Zoo, Brownsville, TX USA
                [11 ]GRID grid.1009.8, ISNI 0000 0004 1936 826X, Present Address: Menzies Institute for Medical Research, , University of Tasmania, ; Hobart, Tasmania Australia
                Author information
                http://orcid.org/0000-0003-2303-7204
                http://orcid.org/0000-0002-9774-1539
                http://orcid.org/0000-0002-1704-9199
                http://orcid.org/0000-0003-3124-3550
                http://orcid.org/0000-0002-5001-6524
                http://orcid.org/0000-0002-6075-8855
                Article
                1656
                10.1038/s42003-021-01656-7
                7851172
                33526843
                27a081c1-5abc-45fe-ac7d-6999b3acadc3
                © 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 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
                : 3 June 2020
                : 31 December 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000735, University of Cambridge;
                Award ID: Gates Cambridge PhD scholarship
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100002715, Sea Turtle Conservancy (STC);
                Award ID: 17-033R
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100007665, Save Our Seas Foundation (SOSF);
                Award ID: SOSF 356
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Award ID: 663830-BU115
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001596, Irish Research Council for Science, Engineering and Technology (IRCSET);
                Award ID: GOIPG/2020/1056
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                cancer,ecology,cancer genomics,cancer models,conservation biology

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