0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics

      research-article

      Read this article at

      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.

          Significance

          Mixotrophy is a ubiquitous nutritional strategy in marine ecosystems. Although our understanding of the distribution and abundance of mixotrophic plankton has improved significantly, the functional roles of mixotrophs are difficult to pinpoint, as mixotroph nutritional strategies are flexible and form a continuum between heterotrophy and phototrophy. We developed a machine learning–based method to assess the nutritional strategies of in situ planktonic populations based on metatranscriptomic profiles. We demonstrate that mixotrophic populations play varying functional roles along physicochemical gradients in the North Pacific Ocean, revealing a degree of physiological plasticity unique to aquatic mixotrophs. Our results highlight mechanisms that may dictate the flow of biogeochemical elements and ecology of the North Pacific Ocean, one of Earth's largest biogeographical provinces.

          Abstract

          Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in the ocean. Their growth, demise, and interactions with other microorganisms drive the fluxes of biogeochemical elements through marine ecosystems. Mixotrophic protists are capable of both photosynthesis and ingestion of prey and are dominant components of open-ocean planktonic communities. Yet the role of mixotrophs in elemental cycling is obscured by their capacity to act as primary producers or heterotrophic consumers depending on factors that remain largely uncharacterized. Here, we develop and apply a machine learning model that predicts the in situ trophic mode of aquatic protists based on their patterns of gene expression. This approach leverages a public collection of protist transcriptomes as a training set to identify a subset of gene families whose transcriptional profiles predict trophic mode. We applied our model to nearly 100 metatranscriptomes obtained during two oceanographic cruises in the North Pacific and found community-level and population-specific evidence that abundant open-ocean mixotrophic populations shift their predominant mode of nutrient and carbon acquisition in response to natural gradients in nutrient supply and sea surface temperature. Metatranscriptomic data from ship-board incubation experiments revealed that abundant mixotrophic prymnesiophytes from the oligotrophic North Pacific subtropical gyre rapidly remodeled their transcriptome to enhance photosynthesis when supplied with limiting nutrients. Coupling this approach with experiments designed to reveal the mechanisms driving mixotroph physiology provides an avenue toward understanding the ecology of mixotrophy in the natural environment.

          Related collections

          Most cited references75

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                10 February 2022
                15 February 2022
                10 February 2022
                : 119
                : 7
                : e2100916119
                Affiliations
                [1] aSchool of Oceanography, University of Washington , Seattle, WA 98195;
                [2] bDepartment of Biology, University of Florida , Gainesville, FL 32611;
                [3] cGenetics Institute, University of Florida , Gainesville, FL 32611;
                [4] dDepartment of Biological Sciences, University of Arkansas , Fayetteville, AR 72701;
                [5] eDepartment of Oceanography, University of Hawai’i at Manoa , Honolulu, HI 96822
                Author notes
                1To whom correspondence may be addressed. Email: lambertb@ 123456uw.edu .

                Edited by W. Doolittle, Department of Chemistry and Molecular Biology, Dalhousie University, Halifax, NS, Canada; received January 18, 2021; accepted December 13, 2021

                Author contributions: B.S.L. and E.V.A. designed research; B.S.L., R.D.G., M.J.S., S.N.C., B.P.D., A.J.A., and A.E.W. performed research; B.S.L. contributed new reagents/analytic tools; B.S.L., R.D.G., and M.J.S. analyzed data; and B.S.L. and E.V.A. wrote the paper.

                Author information
                https://orcid.org/0000-0002-4831-3255
                https://orcid.org/0000-0001-7874-7217
                https://orcid.org/0000-0003-4669-0740
                https://orcid.org/0000-0001-9422-8388
                https://orcid.org/0000-0002-2253-157X
                https://orcid.org/0000-0003-1241-2654
                https://orcid.org/0000-0002-0938-7948
                https://orcid.org/0000-0001-7865-5101
                Article
                202100916
                10.1073/pnas.2100916119
                8851463
                35145022
                69058647-3fd4-4095-8bed-e34ae3918af1
                Copyright © 2022 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 13 December 2021
                Page count
                Pages: 11
                Funding
                Funded by: Simons Foundation 100000893
                Award ID: #594111
                Award Recipient : Bennett S. Lambert Award Recipient : Angelicque E White Award Recipient : E Virginia Armbrust
                Funded by: Simons Foundation 100000893
                Award ID: #426570SP
                Award Recipient : Bennett S. Lambert Award Recipient : Angelicque E White Award Recipient : E Virginia Armbrust
                Funded by: XSEDE
                Award ID: OCE160019
                Award Recipient : Ryan D. Groussman
                Categories
                414
                Biological Sciences
                Ecology

                machine learning,mixotrophy,metatranscriptomics,microbial ecology,trophic mode

                Comments

                Comment on this article