16
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Probing the evolution, ecology and physiology of marine protists using transcriptomics

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Protists, which are single-celled eukaryotes, critically influence the ecology and chemistry of marine ecosystems, but genome-based studies of these organisms have lagged behind those of other microorganisms. However, recent transcriptomic studies of cultured species, complemented by meta-omics analyses of natural communities, have increased the amount of genetic information available for poorly represented branches on the tree of eukaryotic life. This information is providing insights into the adaptations and interactions between protists and other microorganisms and macroorganisms, but many of the genes sequenced show no similarity to sequences currently available in public databases. A better understanding of these newly discovered genes will lead to a deeper appreciation of the functional diversity and metabolic processes in the ocean. In this Review, we summarize recent developments in our understanding of the ecology, physiology and evolution of protists, derived from transcriptomic studies of cultured strains and natural communities, and discuss how these novel large-scale genetic datasets will be used in the future.

          Related collections

          Most cited references110

          • Record: found
          • Abstract: found
          • Article: not found

          Ocean plankton. Determinants of community structure in the global plankton interactome.

          Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Environmental science. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes.

            The profound influence of marine plankton on the global carbon cycle has been recognized for decades, particularly for photosynthetic microbes that form the base of ocean food chains. However, a comprehensive model of the carbon cycle is challenged by unicellular eukaryotes (protists) having evolved complex behavioral strategies and organismal interactions that extend far beyond photosynthetic lifestyles. As is also true for multicellular eukaryotes, these strategies and their associated physiological changes are difficult to deduce from genome sequences or gene repertoires—a problem compounded by numerous unknown function proteins. Here, we explore protistan trophic modes in marine food webs and broader biogeochemical influences. We also evaluate approaches that could resolve their activities, link them to biotic and abiotic factors, and integrate them into an ecosystems biology framework.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Revisiting global gene expression analysis.

              Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease, and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms. Copyright © 2012 Elsevier Inc. All rights reserved.
                Bookmark

                Author and article information

                Journal
                Nature Reviews Microbiology
                Nat Rev Microbiol
                Springer Science and Business Media LLC
                1740-1526
                1740-1534
                January 2017
                November 21 2016
                January 2017
                : 15
                : 1
                : 6-20
                Article
                10.1038/nrmicro.2016.160
                27867198
                d1019b93-8bb8-4f6c-9187-a4802451bb92
                © 2017

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article