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      Gene Expression Variation Resolves Species and Individual Strains among Coral-Associated Dinoflagellates within the Genus Symbiodinium

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          Abstract

          Reef-building corals depend on symbiotic mutualisms with photosynthetic dinoflagellates in the genus Symbiodinium. This large microalgal group comprises many highly divergent lineages (“Clades A–I”) and hundreds of undescribed species. Given their ecological importance, efforts have turned to genomic approaches to characterize the functional ecology of Symbiodinium. To date, investigators have only compared gene expression between representatives from separate clades—the equivalent of contrasting genera or families in other dinoflagellate groups—making it impossible to distinguish between clade-level and species-level functional differences. Here, we examined the transcriptomes of four species within one Symbiodinium clade (Clade B) at ∼20,000 orthologous genes, as well as multiple isoclonal cell lines within species (i.e., cultured strains). These species span two major adaptive radiations within Clade B, each encompassing both host-specialized and ecologically cryptic taxa. Species-specific expression differences were consistently enriched for photosynthesis-related genes, likely reflecting selection pressures driving niche diversification. Transcriptional variation among strains involved fatty acid metabolism and biosynthesis pathways. Such differences among individuals are potentially a major source of physiological variation, contributing to the functional diversity of coral holobionts composed of unique host–symbiont genotype pairings. Our findings expand the genomic resources available for this important symbiont group and emphasize the power of comparative transcriptomics as a method for studying speciation processes and interindividual variation in nonmodel organisms.

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          Ecological consequences of genetic diversity.

          Understanding the ecological consequences of biodiversity is a fundamental challenge. Research on a key component of biodiversity, genetic diversity, has traditionally focused on its importance in evolutionary processes, but classical studies in evolutionary biology, agronomy and conservation biology indicate that genetic diversity might also have important ecological effects. Our review of the literature reveals significant effects of genetic diversity on ecological processes such as primary productivity, population recovery from disturbance, interspecific competition, community structure, and fluxes of energy and nutrients. Thus, genetic diversity can have important ecological consequences at the population, community and ecosystem levels, and in some cases the effects are comparable in magnitude to the effects of species diversity. However, it is not clear how widely these results apply in nature, as studies to date have been biased towards manipulations of plant clonal diversity, and little is known about the relative importance of genetic diversity vs. other factors that influence ecological processes of interest. Future studies should focus not only on documenting the presence of genetic diversity effects but also on identifying underlying mechanisms and predicting when such effects are likely to occur in nature.
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            Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

            Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location. Copyright 2001 Academic Press.
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              Streaming fragment assignment for real-time analysis of sequencing experiments

              We present eXpress, a software package for highly efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time, and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data, showing greater efficiency than other quantification methods.
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                Author and article information

                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                gbe
                Genome Biology and Evolution
                Oxford University Press
                1759-6653
                March 2016
                11 February 2016
                11 February 2016
                : 8
                : 3
                : 665-680
                Affiliations
                [1 ]Department of Biology, Pennsylvania State University
                [2 ]Red Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
                Author notes

                Data deposition: This project has been deposited at the NCBI Sequence Read Archive database under accession numbers PRJNA274852, PRJNA274854, PRJNA274855, and PRJNA274856, and the RNAseq transcriptomes at reefgenomics.org.

                Associate editor: Eric Bapteste

                Article
                evw019
                10.1093/gbe/evw019
                4824173
                26868597
                bd7cdd82-d3f1-4d7d-85ff-3321ff625ff5
                © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 01 February 2016
                Page count
                Pages: 16
                Categories
                Research Article

                Genetics
                dinoflagellates,phylogenetics,rnaseq,symbiosis,transcriptome,zooxanthellae
                Genetics
                dinoflagellates, phylogenetics, rnaseq, symbiosis, transcriptome, zooxanthellae

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