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      Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals

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          Abstract

          Mammals diversified by colonizing drastically different environments, with each transition yielding numerous molecular changes including losses of protein function. While not initially deleterious, these losses could subsequently carry deleterious pleiotropic consequences. Here we use phylogenetic methods to identify convergent functional losses across independent marine mammal lineages. In one extreme case, Paraoxonase 1 ( PON1) accrued lesions in all marine lineages, while remaining intact in all terrestrial mammals. These lesions coincide with PON1 enzymatic activity loss in marine species’ blood plasma. This convergent loss is likely explained by parallel shifts in marine ancestors’ lipid metabolism and/or bloodstream oxidative environment affecting PON1’s role in fatty acid oxidation. PON1 loss also eliminates marine mammals’ main defense against neurotoxicity from specific man-made organophosphorus compounds, implying potential risks in modern environments.

          One Sentence Summary:

          Organophosphate toxicity may threaten modern marine mammals due to their ancestors’ repeated loss of PON1 for oxidative or metabolic reasons.

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          Most cited references86

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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                Author and article information

                Journal
                0404511
                7473
                Science
                Science
                Science (New York, N.Y.)
                0036-8075
                1095-9203
                6 November 2018
                10 August 2018
                10 February 2019
                : 361
                : 6402
                : 591-594
                Affiliations
                [1 ]Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
                [2 ]Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
                [3 ]Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
                [4 ]Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
                [5 ]Wetland and Aquatic Research Center, U.S. Geological Survey, Gainesville, FL, USA.
                [6 ]Department of Biology, Sonoma State University, Rohnert Park, CA, USA.
                [7 ]Pittsburgh Zoo & PPG Aquarium, Pittsburgh, PA, USA.
                [8 ]School of Biological Sciences, The University of Queensland, St Lucia, 4072, QLD, AUST.
                [9 ]Department of Genome Sciences, University of Washington, Seattle, WA, USA.
                [10 ]Pittsburgh Center for Evolutionary Biology and Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
                Author notes

                Author contributions: NLC, MC, CEF, and WKM designed the study. RB, DEC, JG, JML, and CEF provided samples and reagents. JJ, JM, and RR performed laboratory experiments. WKM, JJ, RP, AK, CK, and NLC performed analyses. WKM, SEW, RP, AK, and NLC generated figures. WKM, CEF, and NLC wrote the paper.

                [* ]Correspondence to: nclark@ 123456pitt.edu .
                Article
                PMC6317340 PMC6317340 6317340 nihpa995232
                10.1126/science.aap7714
                6317340
                30093596
                1539993d-ea86-4368-869c-dac18db155ac
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