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      Insights into the ecological roles and evolution of methyl-coenzyme M reductase-containing hot spring Archaea

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          Abstract

          Several recent studies have shown the presence of genes for the key enzyme associated with archaeal methane/alkane metabolism, methyl-coenzyme M reductase (Mcr), in metagenome-assembled genomes (MAGs) divergent to existing archaeal lineages. Here, we study the mcr-containing archaeal MAGs from several hot springs, which reveal further expansion in the diversity of archaeal organisms performing methane/alkane metabolism. Significantly, an MAG basal to organisms from the phylum Thaumarchaeota that contains mcr genes, but not those for ammonia oxidation or aerobic metabolism, is identified. Together, our phylogenetic analyses and ancestral state reconstructions suggest a mostly vertical evolution of mcrABG genes among methanogens and methanotrophs, along with frequent horizontal gene transfer of mcr genes between alkanotrophs. Analysis of all mcr-containing archaeal MAGs/genomes suggests a hydrothermal origin for these microorganisms based on optimal growth temperature predictions. These results also suggest methane/alkane oxidation or methanogenesis at high temperature likely existed in a common archaeal ancestor.

          Abstract

          Methane metabolism by some lineages of Archaea contributes to the cycling of carbon on Earth. Here, the authors show high diversity of methyl-coenzyme M reductase (Mcr), a key enzyme associated with archaeal methane/alkane metabolism, in hot spring Archaea, and investigate their ecological roles and evolution.

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          Most cited references81

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

            The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              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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                Author and article information

                Contributors
                g.tyson@uq.edu.au
                zhangt@hku.hk
                liwenjun3@mail.sysu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 October 2019
                8 October 2019
                2019
                : 10
                : 4574
                Affiliations
                [1 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, State Key Laboratory of Biocontrol, Guangdong Provincial Key Laboratory of Plant Resources and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Life Sciences, , Sun Yat-Sen University, ; 510275 Guangzhou, PR China
                [2 ]ISNI 0000 0001 2179 2404, GRID grid.254880.3, Department of Biological Sciences, , Dartmouth College, ; Hanover, NH 03755 USA
                [3 ]ISNI 0000000121742757, GRID grid.194645.b, Environmental Microbiome Engineering and Biotechnology Laboratory, Department of Civil Engineering, , The University of Hong Kong, ; 999077 Hong Kong, SAR PR China
                [4 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, The Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, , University of Queensland, ; St Lucia, 4072 QLD Australia
                [5 ]ISNI 0000 0001 2296 1505, GRID grid.410877.d, Faculty of Biosciences and Medical Engineering, , Universiti Teknologi Malaysia, ; Skudai, Johor 81310 Malaysia
                [6 ]ISNI 0000 0001 0472 9649, GRID grid.263488.3, College of Chemistry and Environmental Engineering, , Shenzhen University, ; 518060 Shenzhen, PR China
                [7 ]ISNI 0000 0004 0368 7397, GRID grid.263785.d, School of Life Sciences, , South China Normal University, ; 510631 Guangzhou, PR China
                [8 ]ISNI 0000 0004 1773 5396, GRID grid.56302.32, Bioproducts Research Chair, Zoology Department, College of Science, , King Saud University, ; Riyadh, 11451 Saudi Arabia
                [9 ]ISNI 0000 0004 0412 4932, GRID grid.411662.6, Botany and Microbiology Department, Faculty of Science, , Beni-Suef University, ; Beni-Suef, 65211 Egypt
                [10 ]ISNI 0000 0001 0806 6926, GRID grid.272362.0, School of Life Sciences, , University of Nevada Las Vegas, ; Las Vegas, NV 89154 USA
                [11 ]ISNI 0000 0001 0806 6926, GRID grid.272362.0, Nevada Institute of Personalized Medicine, , University of Nevada Las Vegas, ; Las Vegas, NV 89154 USA
                [12 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, Advanced Water Management Centre, , University of Queensland, ; St Lucia, 4072 QLD Australia
                [13 ]ISNI 0000 0001 0038 6319, GRID grid.458469.2, Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, ; 830011 Urumqi, PR China
                Author information
                http://orcid.org/0000-0002-2839-8722
                http://orcid.org/0000-0002-3895-2301
                http://orcid.org/0000-0003-2467-9719
                http://orcid.org/0000-0001-8530-0448
                http://orcid.org/0000-0001-8559-9427
                http://orcid.org/0000-0002-1233-736X
                Article
                12574
                10.1038/s41467-019-12574-y
                6783470
                31594929
                3867da38-5f25-4a5c-85a6-15192c6f6d02
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 December 2018
                : 12 September 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: U1201233
                Award ID: 31370154
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004751, Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&T for Social Development);
                Award ID: 2015FY110100
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                microbial ecology,molecular evolution,archaeal genes,archaea
                Uncategorized
                microbial ecology, molecular evolution, archaeal genes, archaea

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