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      Innate immunity in the simplest animals – placozoans

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          Abstract

          Background

          Innate immunity provides the core recognition system in animals for preventing infection, but also plays an important role in managing the relationship between an animal host and its symbiont. Most of our knowledge about innate immunity stems from a few animal model systems, but substantial variation between metazoan phyla has been revealed by comparative genomic studies. The exploration of more taxa is still needed to better understand the evolution of immunity related mechanisms. Placozoans are morphologically the simplest organized metazoans and the association between these enigmatic animals and their rickettsial endosymbionts has recently been elucidated. Our analyses of the novel placozoan nuclear genome of Trichoplax sp. H2 and its associated rickettsial endosymbiont genome clearly pointed to a mutualistic and co-evolutionary relationship. This discovery raises the question of how the placozoan holobiont manages symbiosis and, conversely, how it defends against harmful microorganisms. In this study, we examined the annotated genome of Trichoplax sp. H2 for the presence of genes involved in innate immune recognition and downstream signaling.

          Results

          A rich repertoire of genes belonging to the Toll-like and NOD-like receptor pathways, to scavenger receptors and to secreted fibrinogen-related domain genes was identified in the genome of Trichoplax sp. H2. Nevertheless, the innate immunity related pathways in placozoans deviate in several instances from well investigated vertebrates and invertebrates. While true Toll- and NOD-like receptors are absent, the presence of many genes of the downstream signaling cascade suggests at least primordial Toll-like receptor signaling in Placozoa. An abundance of scavenger receptors, fibrinogen-related domain genes and Apaf-1 genes clearly constitutes an expansion of the immunity related gene repertoire specific to Placozoa.

          Conclusions

          The found wealth of immunity related genes present in Placozoa is surprising and quite striking in light of the extremely simple placozoan body plan and their sparse cell type makeup. Research is warranted to reveal how Placozoa utilize this immune repertoire to manage and maintain their associated microbiota as well as to fend-off pathogens.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-5377-3) contains supplementary material, which is available to authorized users.

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          Most cited references49

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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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            Protein homology detection by HMM-HMM comparison.

            Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolution. We have generalized the alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs. We present a method for detecting distant homologous relationships between proteins based on this approach. The method (HHsearch) is benchmarked together with BLAST, PSI-BLAST, HMMER and the profile-profile comparison tools PROF_SIM and COMPASS, in an all-against-all comparison of a database of 3691 protein domains from SCOP 1.63 with pairwise sequence identities below 20%.Sensitivity: When the predicted secondary structure is included in the HMMs, HHsearch is able to detect between 2.7 and 4.2 times more homologs than PSI-BLAST or HMMER and between 1.44 and 1.9 times more than COMPASS or PROF_SIM for a rate of false positives of 10%. Approximately half of the improvement over the profile-profile comparison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an increased alignment quality. HHsearch produced 1.2, 1.7 and 3.3 times more good alignments ('balanced' score >0.3) than the next best method (COMPASS), and 1.6, 2.9 and 9.4 times more than PSI-BLAST, at the family, superfamily and fold level, respectively.Speed: HHsearch scans a query of 200 residues against 3691 domains in 33 s on an AMD64 2GHz PC. This is 10 times faster than PROF_SIM and 17 times faster than COMPASS.
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              OrthoMCL: identification of ortholog groups for eukaryotic genomes.

              The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
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                Author and article information

                Contributors
                0049 511 953 8402 , kai.kamm@ecolevol.de
                bernd.schierwater@ecolevol.de
                desalle@amnh.org
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                5 January 2019
                5 January 2019
                2019
                : 20
                : 5
                Affiliations
                [1 ]ISNI 0000 0001 0126 6191, GRID grid.412970.9, ITZ Ecology and Evolution, , University of Veterinary Medicine Hannover, Foundation, ; Bünteweg 17d, D-30559 Hannover, Germany
                [2 ]ISNI 0000 0001 2152 1081, GRID grid.241963.b, Sackler Institute for Comparative Genomics and Division of Invertebrate Zoology, , American Museum of Natural History, ; New York, NY USA
                [3 ]ISNI 0000000419368710, GRID grid.47100.32, Ecology and Evolutionary Biology, , Yale University, ; New Haven, CT 06520 USA
                Author information
                http://orcid.org/0000-0001-8038-2843
                Article
                5377
                10.1186/s12864-018-5377-3
                6321704
                30611207
                06c8b2b9-c254-42e5-bf81-c07af27a7ba8
                © 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
                : 7 August 2018
                : 16 December 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: Schi-277/26
                Award ID: Schi-277/27
                Award ID: Schi-277/29
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Genetics
                innate immunity,placozoa,trichoplax,symbiosis,toll-like receptor pathway,nod-like receptor pathway,scavenger receptors,fibrinogen-related proteins

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