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      The Capsaspora genome reveals a complex unicellular prehistory of animals

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          Abstract

          To reconstruct the evolutionary origin of multicellular animals from their unicellular ancestors, the genome sequences of diverse unicellular relatives are essential. However, only the genome of the choanoflagellate Monosiga brevicollis has been reported to date. Here we completely sequence the genome of the filasterean Capsaspora owczarzaki, the closest known unicellular relative of metazoans besides choanoflagellates. Analyses of this genome alter our understanding of the molecular complexity of metazoans’ unicellular ancestors showing that they had a richer repertoire of proteins involved in cell adhesion and transcriptional regulation than previously inferred only with the choanoflagellate genome. Some of these proteins were secondarily lost in choanoflagellates. In contrast, most intercellular signalling systems controlling development evolved later concomitant with the emergence of the first metazoans. We propose that the acquisition of these metazoan-specific developmental systems and the co-option of pre-existing genes drove the evolutionary transition from unicellular protists to metazoans.

          Abstract

          Unicellular ancestors of metazoans can provide significant insights into the origin of multicellularity. Suga et al. present the first complete genome of the filasterean Capsaspora owczarzaki and suggest an evolutionary mechanism for the transition from unicellular protists to metazoans.

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          Most cited references26

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          Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization.

          Sea anemones are seemingly primitive animals that, along with corals, jellyfish, and hydras, constitute the oldest eumetazoan phylum, the Cnidaria. Here, we report a comparative analysis of the draft genome of an emerging cnidarian model, the starlet sea anemone Nematostella vectensis. The sea anemone genome is complex, with a gene repertoire, exon-intron structure, and large-scale gene linkage more similar to vertebrates than to flies or nematodes, implying that the genome of the eumetazoan ancestor was similarly complex. Nearly one-fifth of the inferred genes of the ancestor are eumetazoan novelties, which are enriched for animal functions like cell signaling, adhesion, and synaptic transmission. Analysis of diverse pathways suggests that these gene "inventions" along the lineage leading to animals were likely already well integrated with preexisting eukaryotic genes in the eumetazoan progenitor.
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            A new generation of homology search tools based on probabilistic inference.

            Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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              The Amphimedon queenslandica genome and the evolution of animal complexity.

              Sponges are an ancient group of animals that diverged from other metazoans over 600 million years ago. Here we present the draft genome sequence of Amphimedon queenslandica, a demosponge from the Great Barrier Reef, and show that it is remarkably similar to other animal genomes in content, structure and organization. Comparative analysis enabled by the sequencing of the sponge genome reveals genomic events linked to the origin and early evolution of animals, including the appearance, expansion and diversification of pan-metazoan transcription factor, signalling pathway and structural genes. This diverse 'toolkit' of genes correlates with critical aspects of all metazoan body plans, and comprises cell cycle control and growth, development, somatic- and germ-cell specification, cell adhesion, innate immunity and allorecognition. Notably, many of the genes associated with the emergence of animals are also implicated in cancer, which arises from defects in basic processes associated with metazoan multicellularity.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                14 August 2013
                : 4
                : 2325
                Affiliations
                [1 ]Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Passeig Marítim de la Barceloneta 37-49 , 08003 Barcelona, Spain
                [2 ]Broad Institute of Harvard and the Massachusetts Institute of Technology , Cambridge, Massachusetts 02142, USA
                [3 ]Department of Biochemistry and Molecular Biology, Faculty of Medicine, Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University , Halifax, Nova Scotia, Canada B3H 1X5
                [4 ]Razavi Newman Center for Bioinformatics, Salk Institute for Biological Studies , 10010 North Torrey Pines Road, La Jolla, California 92037, USA
                [5 ]School of Applied Sciences, University of Huddersfield , Huddersfield HD1 3DH, UK
                [6 ]Institut Jacques Monod, CNRS, UMR 7592, Univ Paris Diderot, Sorbonne Paris Cité , F-75205 Paris, France
                [7 ]Centre for Genomic Regulation (CRG) , Dr Aiguader 88, 08003 Barcelona, Spain
                [8 ]Département de Biochimie, Centre Robert-Cedergren, Université de Montréal, 2900 Boulevard Edouard Montpetit , Montréal (Québec), Canada H3C 3J7
                [9 ]Departament de Genètica, Facultat de Biologia, Universitat de Barcelona , Avinguda Diagonal 643, 08028 Barcelona, Spain
                [10 ]Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluís Companys, 23, 08010 Barcelona, Spain
                [11 ]These authors contributed equally to this work
                Author notes
                Article
                ncomms3325
                10.1038/ncomms3325
                3753549
                23942320
                63a8e2a4-9da4-4d9f-a25e-dd546ca561e8
                Copyright © 2013, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

                History
                : 18 June 2013
                : 18 July 2013
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