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Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment

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      Abstract

      Microorganisms in the subsurface represent a substantial but poorly understood component of the Earth’s biosphere. Subsurface environments are complex and difficult to characterize; thus, their microbiota have remained as a ‘dark matter’ of the carbon and other biogeochemical cycles. Here we deeply sequence two sediment-hosted microbial communities from an aquifer adjacent to the Colorado River, CO, USA. No single organism represents more than ~1% of either community. Remarkably, many bacteria and archaea in these communities are novel at the phylum level or belong to phyla lacking a sequenced representative. The dominant organism in deeper sediment, RBG-1, is a member of a new phylum. On the basis of its reconstructed complete genome, RBG-1 is metabolically versatile. Its wide respiration-based repertoire may enable it to respond to the fluctuating redox environment close to the water table. We document extraordinary microbial novelty and the importance of previously unknown lineages in sediment biogeochemical transformations.

      Abstract

      Turnover of sediment organic matter contributes to global carbon cycling, yet the microorganisms involved are largely unknown. Castelle et al. reveal that an aquifer sediment core hosts a ‘zoo’ of organisms, including representatives of a previously undescribed phylum (Zixibacteria).

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      Most cited references 59

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      MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

      Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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        MUSCLE: multiple sequence alignment with high accuracy and high throughput.

         Robert Edgar (2004)
        We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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          A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

          The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.
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            Author and article information

            Affiliations
            [1 ]Department of Earth and Planetary Science, University of California , Berkeley, California 94720, USA
            [2 ]Earth Sciences Division, Lawrence Berkeley National Laboratory , Berkeley (LBNL), California 94720, USA
            [3 ]UC Davis Genome Center, University of California, Davis , Davis, California 95616, USA
            [4 ]Department of Energy Joint Genome Institute , Walnut Creek, California 94598, USA
            [5 ]Genomics Division, Lawrence Berkeley National Laboratory , Berkeley (LBNL), California 94720, USA
            [6 ]Department of Evolution and Ecology, University of California, Davis , Davis, California 95616, USA
            [7 ]Department of Medical Microbiology and Immunology, University of California, Davis , Davis, California 95616, USA
            Author notes
            Journal
            Nat Commun
            Nat Commun
            Nature Communications
            Nature Pub. Group
            2041-1723
            27 August 2013
            : 4
            23979677
            3903129
            ncomms3120
            10.1038/ncomms3120
            Copyright © 2013, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

            This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/

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