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Despite being perennially frigid, polar oceans form an ecosystem hosting high and unique biodiversity. Various organisms show different adaptative strategies in this habitat, but how viruses adapt to this environment is largely unknown. Viruses of phylaNucleocytoviricotaandMirusviricotaare groups of eukaryote-infecting large and giant DNA viruses with genomes encoding a variety of functions. Here, by leveraging the Global Ocean Eukaryotic Viral database, we investigate the biogeography and functional repertoire of these viruses at a global scale. We first confirm the existence of an ecological barrier that clearly separates polar and nonpolar viral communities, and demonstrate that temperature drives dramatic changes in the virus–host network at the polar/nonpolar boundary. Ancestral niche reconstruction suggests that adaptation of these viruses to polar conditions has occurred repeatedly over the course of evolution, with polar-adapted viruses in the modern ocean being scattered across their phylogeny. Numerous viral genes are specifically associated with polar adaptation, although most of their homologues are not identified as polar-adaptive genes in eukaryotes. These results suggest that giant viruses adapt to cold environments by changing their functional repertoire, and this viral evolutionary strategy is independent of the polar adaptation of their hosts.

This document explores the potential of big data, i.e. large volumes of high variety data collected at high velocity, to advance marine science. Marine science is rapidly entering the digital age. Expansions in the scope and scale of ocean observations, as well as automated sampling and ‘smart sensors’, are leading to a continuous flood of data. This provides opportunities to transform the way we study and understand the ocean through more complex and interdisciplinary analyses, and offers novel approaches for the management of marine resources. However, more data do not necessarily mean that we have the right data to answer many critical scientific questions and to make well-informed, data-driven management decisions on the sustainable use of ocean resources. To increase the value of the wealth of marine big data, it must be openly shared, interoperable and integrated into complex transdisciplinary analyses using artificial intelligence.

5th Congress of European Microbiologists (FEMS 2013), 21-25 july 2013, Leipzig, Germany.-- 1 page

9 páginas, 3 figuras, 1 tabla, The rate of information collection generated by metagenomics is uncoupled with its meaningful ecological interpretation. New analytical approaches based on functional trait-based ecology may help to bridge this gap and extend the trait approach to the community level in vast and complex environmental genetic data sets. Here, we explored a set of community traits that range from nucleotidic to genomic properties in 53 metagenomic aquatic samples from the Global Ocean Sampling (GOS) expedition. We found significant differences between the community profile derived from the commonly used 16S rRNA gene and from the functional trait set. The traits proved to be valuable ecological markers by discriminating between marine ecosystems (coastal vs. open ocean) and between oceans (Atlantic vs. Indian vs. Pacific). Intertrait relationships were also assessed, and we propose some that could be further used as habitat descriptors or indicators of artefacts during sample processing. Overall, the approach presented here may help to interpret metagenomics data to gain a full understanding of microbial community patterns in a rigorous ecological framework., AB is supported by the Spanish FPU predoctoral scholarship program and EOC laboratory by grants PIRENA CGL2009-13318 from the Spanish Ministerio de Ciencia e Innovación (MICINN) and the EU-COST Action number ES1103: Microbial Ecology & The Earth System: Collaborating for Insight and Success with the new generation of sequencing tools (CISME).

This document explores the potential of big data, i.e. large volumes of high variety data collected at high velocity, to advance marine science. Marine science is rapidly entering the digital age. Expansions in the scope and scale of ocean observations, as well as automated sampling and ‘smart sensors’, are leading to a continuous flood of data. This provides opportunities to transform the way we study and understand the ocean through more complex and interdisciplinary analyses, and offers novel approaches for the management of marine resources. However, more data do not necessarily mean that we have the right data to answer many critical scientific questions and to make well-informed, data-driven management decisions on the sustainable use of ocean resources. To increase the value of the wealth of marine big data, it must be openly shared, interoperable and integrated into complex transdisciplinary analyses using artificial intelligence.