Symbiosis is a widespread phenomenon in the biosphere. In marine benthic environments breakdown of symbiosis is responsible for coral bleaching and dramatically affects coral reef ecosystems functioning [1]. In the water column, planktonic organisms are key component of pelagic ecosystems and a number of species form mutualistic association with microalgae forming a photosymbiosis [2].

Here we intended to investigate the genetic basis of photosymbiosis, through a transcriptomic approach on marine plankton organisms. We focused more specifically on associations occurring between radiolarian host (protist, zooplankton) and dinoflagellates symbiont (protist, phytoplankton) living inside the host cell. It has recently been highlighted that these holobionts are widespread in oligotrophic open oceans and there are also evidences of their fundamental implication in biogeochemical carbon, silica and strontium cycles [3, 4, 5, 6].

RNA-seq technologies allow obtaining an unprecedented amount of data for unicellular organisms isolated from the environment [7]. The study of such non-model holobionts datasets requires de novo assembly, which implies considerable computational resources and the potential production of chimeric sequences [8, 9].

We therefore developed an original strategy aiming at accelerating and improving de novo assembly for holobiont datasets. We chose SIMKA [10] a fast kmer-based method initially developed to estimate the similarity between numerous metagenomic datasets, and which has been recently adapted to extract their common sequences. As our symbionts are identified, we used SIMKA to compare our holobionts transcriptomes to publicly available dinoflagellates transcriptomes [11] generating two datasets, one composed of reads from the symbionts and another with reads from the host (for which no reference data are currently available). Independent assemblies were then performed in parallel, accelerating the study process, and minimizing the proportion of resulting chimeric sequences. Our strategy produced a unique and comprehensive genomic dataset for Radiolaria [12, 13], and offers a pragmatic, large scale, comparison strategy to assemble and study holobionts [9]. Our new sequences obtained from holobionts study will be used for phylogenomics investigation, as reference for environmental metagenomic studies and ultimately to understand and characterize the molecular basis of symbiotic relationships in the plankton.

Bibliographic references

[1] Simon K. Davy, Denis Allemand, and Virginia M. Weis. “Cell Biology of Cnidarian-Dinoflagellate Symbiosis”. In: Microbiology and Molecular Biology Reviews 76.2 (June 1, 2012), pp. 229–261. issn: 1092-2172, 1098- 5557. doi: 10.1128/MMBR.05014-11.

[2] Janouškovec, J. et al. “Major transitions in dinoflagellate evolution unveiled by phylotranscriptomics”. in: PNAS 201614842 (2016). doi:10.1073/pnas.1614842114

[3] Tristan Biard et al. “In situ imaging reveals the biomass of giant protists in the global ocean”. In: Nature advance online publication (Apr. 20, 2016). issn: 0028-0836. doi: 10.1038/nature17652.

[4] Lionel Guidi et al. “Plankton networks driving carbon export in the olig- otrophic ocean”. In: Nature 532.7600 (Apr. 28, 2016), pp. 465–470. issn: 0028-0836. doi: 10.1038/nature16942.

[5] Diane K. Stoecker et al. “Acquired phototrophy in aquatic protists”. In: Aquatic Microbial Ecology 57.3 (Nov. 24, 2009), pp. 279–310. doi: 10.3354/ame01340.

[6] Johan Decelle et al. “An original mode of symbiosis in open ocean plankton”. In: Proceedings of the National Academy of Sciences 109.44 (Oct. 30, 2012), pp. 18000–18005. issn: 0027-8424, 1091-6490. doi: 10.1073/pnas.1212303109.

[7] Sergio Balzano et al. “Transcriptome analyses to investigate symbiotic relationships between marine protists”. In: Frontiers in Microbiology 6 (Mar. 17, 2015). issn: 1664-302X. doi: 10.3389/fmicb.2015.00098.

[8] Li, B. et al. “Evaluation of de novo transcriptome assemblies from RNA-Seq data”. In: Genome Biology 15, 553 (2014).

[9] Sangwan, N., Xia, F. & Gilbert, J. A. “Recovering complete and draft population genomes from metagenome datasets”. In: Microbiome 4, 8 (2016).

[10] Gaëtan Benoit et al. “Multiple comparative metagenomics using mul- tiset k -mer counting”. In: PeerJ Computer Science 2 (Nov. 14, 2016). doi: 10.7717/peerj-cs.94.

[11] Patrick J. Keeling et al. “The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing”. In: PLOS Biol 12.6 (June 2014), e1001889. issn: 1545-7885. doi: 10.1371/journal.pbio.1001889.

[12] Burki, F. et al. “Evolution of Rhizaria: new insights from phylogenomic analysis of uncultured protists”. In BMC Evolutionary Biology 10, 377 (2010).