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      Emergence of trait variability through the lens of nitrogen assimilation in Prochlorococcus

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          Abstract

          Intraspecific trait variability has important consequences for the function and stability of marine ecosystems. Here we examine variation in the ability to use nitrate across hundreds of Prochlorococcus genomes to better understand the modes of evolution influencing intraspecific allocation of ecologically important functions. Nitrate assimilation genes are absent in basal lineages but occur at an intermediate frequency that is randomly distributed within recently emerged clades. The distribution of nitrate assimilation genes within clades appears largely governed by vertical inheritance, gene loss, and homologous recombination. By mapping this process onto a model of Prochlorococcus’ macroevolution, we propose that niche-constructing adaptive radiations and subsequent niche partitioning set the stage for loss of nitrate assimilation genes from basal lineages as they specialized to lower light levels. Retention of these genes in recently emerged lineages has likely been facilitated by selection as they sequentially partitioned into niches where nitrate assimilation conferred a fitness benefit.

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          Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus.

          The Cyanobacteria Prochlorococcus and Synechococcus account for a substantial fraction of marine primary production. Here, we present quantitative niche models for these lineages that assess present and future global abundances and distributions. These niche models are the result of neural network, nonparametric, and parametric analyses, and they rely on >35,000 discrete observations from all major ocean regions. The models assess cell abundance based on temperature and photosynthetically active radiation, but the individual responses to these environmental variables differ for each lineage. The models estimate global biogeographic patterns and seasonal variability of cell abundance, with maxima in the warm oligotrophic gyres of the Indian and the western Pacific Oceans and minima at higher latitudes. The annual mean global abundances of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 10(27) and 7.0 ± 0.3 × 10(26) cells, respectively. Using projections of sea surface temperature as a result of increased concentration of greenhouse gases at the end of the 21st century, our niche models projected increases in cell numbers of 29% and 14% for Prochlorococcus and Synechococcus, respectively. The changes are geographically uneven but include an increase in area. Thus, our global niche models suggest that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high abundances and contributions to primary production of Prochlorococcus and Synechococcus, these changes may have large impacts on ocean ecosystems and biogeochemical cycles.
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            ClonalFrameML: Efficient Inference of Recombination in Whole Bacterial Genomes

            Recombination is an important evolutionary force in bacteria, but it remains challenging to reconstruct the imports that occurred in the ancestry of a genomic sample. Here we present ClonalFrameML, which uses maximum likelihood inference to simultaneously detect recombination in bacterial genomes and account for it in phylogenetic reconstruction. ClonalFrameML can analyse hundreds of genomes in a matter of hours, and we demonstrate its usefulness on simulated and real datasets. We find evidence for recombination hotspots associated with mobile elements in Clostridium difficile ST6 and a previously undescribed 310kb chromosomal replacement in Staphylococcus aureus ST582. ClonalFrameML is freely available at http://clonalframeml.googlecode.com/.
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              PAML: a program package for phylogenetic analysis by maximum likelihood

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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                01 February 2019
                2019
                : 8
                : e41043
                Affiliations
                [1 ]deptDepartment of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeUnited States
                [2 ]Bigelow Laboratory for Ocean Sciences East BoothbayUnited States
                [3 ]deptDepartment of Biology Massachusetts Institute of Technology CambridgeUnited States
                Rutgers University United States
                Max Planck Institute for Chemical Ecology Germany
                Rutgers University United States
                University of Alabama United States
                Author notes
                [†]

                Department of Earth System Science, Stanford University, Palo Alto, United States.

                Author information
                http://orcid.org/0000-0001-5598-6602
                https://orcid.org/0000-0002-0031-2835
                http://orcid.org/0000-0003-4458-3108
                Article
                41043
                10.7554/eLife.41043
                6370341
                30706847
                66c72add-4720-4c75-9e66-3258897a5551
                © 2019, Berube et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 13 August 2018
                : 31 January 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: OCE-1153588
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DBI-0424599
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: OCE-1335810
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 337262
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 329108
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000936, Gordon and Betty Moore Foundation;
                Award ID: GBMF495
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000936, Gordon and Betty Moore Foundation;
                Award ID: GBMF4511
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 509034SCFY17
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Ecology
                Evolutionary Biology
                Custom metadata
                In the context of an organism's ecology, physiology, and macroevolutionary history, inheritance and gene loss can yield emergent patterns of trait variability that give the appearance of gene acquisition.

                Life sciences
                prochlorococcus,synechococcus,cyanobacteria,other
                Life sciences
                prochlorococcus, synechococcus, cyanobacteria, other

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