Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils.
TL;DR: The induction of HAMO activity occurred only after the rapid growth of methanotrophic populations, and a metatranscriptome-wide association study suggests that the concurrent high- and low-affinity methane oxidation was catalysed by known meethanotrophs rather than by the proposed novel atmospheric methane oxidizers.
Abstract: Soils serve as the biological sink of the potent greenhouse gas methane with exceptionally low concentrations of ∼1.84 p.p.m.v. in the atmosphere. The as-yet-uncultivated methane-consuming bacteria have long been proposed to be responsible for this 'high-affinity' methane oxidation (HAMO). Here we show an emerging HAMO activity arising from conventional methanotrophs in paddy soil. HAMO activity was quickly induced during the low-affinity oxidation of high-concentration methane. Activity was lost gradually over 2 weeks, but could be repeatedly regained by flush-feeding the soil with elevated methane. The induction of HAMO activity occurred only after the rapid growth of methanotrophic populations, and a metatranscriptome-wide association study suggests that the concurrent high- and low-affinity methane oxidation was catalysed by known methanotrophs rather than by the proposed novel atmospheric methane oxidizers. These results provide evidence of atmospheric methane uptake in periodically drained ecosystems that are typically considered to be a source of atmospheric methane.
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TL;DR: It is shown in a series of experiments that high-yielding rice cultivars actually reduce CH4 emissions from typical paddy soils, and suggests that modern rice breeding strategies for high-Yielding cultivars can substantially mitigate paddy CH4 emission in China and other rice growing regions.
Abstract: Breeding high-yielding rice cultivars through increasing biomass is a key strategy to meet rising global food demands. Yet, increasing rice growth can stimulate methane (CH4 ) emissions, exacerbating global climate change, as rice cultivation is a major source of this powerful greenhouse gas. Here, we show in a series of experiments that high-yielding rice cultivars actually reduce CH4 emissions from typical paddy soils. Averaged across 33 rice cultivars, a biomass increase of 10% resulted in a 10.3% decrease in CH4 emissions in a soil with a high carbon (C) content. Compared to a low-yielding cultivar, a high-yielding cultivar significantly increased root porosity and the abundance of methane-consuming microorganisms, suggesting that the larger and more porous root systems of high-yielding cultivars facilitated CH4 oxidation by promoting O2 transport to soils. Our results were further supported by a meta-analysis, showing that high-yielding rice cultivars strongly decrease CH4 emissions from paddy soils with high organic C contents. Based on our results, increasing rice biomass by 10% could reduce annual CH4 emissions from Chinese rice agriculture by 7.1%. Our findings suggest that modern rice breeding strategies for high-yielding cultivars can substantially mitigate paddy CH4 emission in China and other rice growing regions.
116 citations
TL;DR: In this paper, a pure culture of a bacterium, strain MG08 that grows on air at atmospheric concentrations of CH4 [1.86 parts per million volume (p.p.m.).
Abstract: The global atmospheric level of methane (CH4), the second most important greenhouse gas, is currently increasing by ∼10 million tons per year. Microbial oxidation in unsaturated soils is the only known biological process that removes CH4 from the atmosphere, but so far, bacteria that can grow on atmospheric CH4 have eluded all cultivation efforts. In this study, we have isolated a pure culture of a bacterium, strain MG08 that grows on air at atmospheric concentrations of CH4 [1.86 parts per million volume (p.p.m.v.)]. This organism, named Methylocapsa gorgona, is globally distributed in soils and closely related to uncultured members of the upland soil cluster α. CH4 oxidation experiments and 13C-single cell isotope analyses demonstrated that it oxidizes atmospheric CH4 aerobically and assimilates carbon from both CH4 and CO2. Its estimated specific affinity for CH4 (a0s) is the highest for any cultivated methanotroph. However, growth on ambient air was also confirmed for Methylocapsa acidiphila and Methylocapsa aurea, close relatives with a lower specific affinity for CH4, suggesting that the ability to utilize atmospheric CH4 for growth is more widespread than previously believed. The closed genome of M. gorgona MG08 encodes a single particulate methane monooxygenase, the serine cycle for assimilation of carbon from CH4 and CO2, and CO2 fixation via the recently postulated reductive glycine pathway. It also fixes dinitrogen and expresses the genes for a high-affinity hydrogenase and carbon monoxide dehydrogenase, suggesting that atmospheric CH4 oxidizers harvest additional energy from oxidation of the atmospheric trace gases carbon monoxide (0.2 p.p.m.v.) and hydrogen (0.5 p.p.m.v.).
116 citations
Cites methods from "Conventional methanotrophs are resp..."
...Numbers for the metabolic steps in the figure refer to the following enzyme names: (1) particulate methane monooxygenase, (2) methanol dehydrogenase and corresponding cytochrome c, (3) formaldehyde activating enzyme, (4) NAD(P)-dependent methylene tetrahydromethanopterin dehydrogenase, (5) methenyl tetrahydromethanopterin cyclohydrolase, (6) formyltransferase/hydrolase, (7A) NAD-dependent formate dehydrogenase, (7B) molybdopterin binding reversible formate dehydrogenase/CO2 reductase, (8) formate-tetrahydrofolate ligase, (9) bifunctional 5,10-methylene-tetrahydrofolate dehydrogenase and 5,10-methylene-tetrahydrofolate cyclohydrolase, (10) glycine cleavage system, (11) serine hydroxymethyltransferase, (12) serine–glyoxylate aminotransferase, (13) hydroxypyruvate reductase, (14) 2-glycerate kinase, (15) enolase, (16) phosphoenolpyruvate carboxylase, (17) malate dehydrogenase, (18) malate thiokinase, (19) malyl-CoA lyase, (20) carbonic anhydrase, (21) acetyl-CoA carboxylase, (22) [MuCo] class I carbon monoxide dehydrogenase, (23) cytochrome c reductase and corresponding cytochrome c, (24) cytochrome c/d/o terminal oxidases (cytochrome d oxidase—cydAB, cytochrome o ubiquinol oxidase—cyoABCD, heme-copper cytochrome c oxidase type A1 – ctaAEGBC, heme-copper cytochrome type C cbb3 oxidase—ccoNOQP and heme-copper cytochrome c oxidase type A1—coxCAB)....
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TL;DR: Metal-AOM is a relatively new research field, and therefore more studies are needed to fully characterize the process, and the many unanswered questions are discussed, which should be useful for future research in this field.
94 citations
Cites background from "Conventional methanotrophs are resp..."
...Methane is quite rare in the atmosphere but is the second most important greenhouse gas, just following carbon dioxide, and contributes about 20% to current global warming (Su et al., 2015; Cai et al., 2016)....
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TL;DR: The release of AgNPs into the environment should be controlled because they interfere with nitrifying communities and stimulate N2O emission and this study highlights the molecular underpinnings of the effects ofAgNPs on nitrification activity.
Abstract: Silver nanoparticles (AgNPs) are the most common materials in nanotechnology-based consumer products globally. Because of the wide application of AgNPs, their potential environmental impact is currently a highly topical focus of concern. Nitrification is one of the processes in the nitrogen cycle most susceptible to AgNPs but the specific effects of AgNPs on nitrification in aquatic environments are not well understood. We report the influence of AgNPs on nitrification and associated nitrous oxide (N2O) production in estuarine sediments. AgNPs inhibited nitrification rates, which decreased exponentially with increasing AgNP concentrations. The response of nitrifier N2O production to AgNPs exhibited low-dose stimulation ( 100% at low doses of AgNPs. This result was confirmed by metatranscriptome studies showing up-regulation of nitric oxide reductase (norQ) gene expression in the low-dose treatment. Isotopomer analysis revealed that hydroxylamine oxidation was the main N2O production pathway, and its contribution to N2O emission was enhanced when exposed to low-dose AgNPs. This study highlights the molecular underpinnings of the effects of AgNPs on nitrification activity and demonstrates that the release of AgNPs into the environment should be controlled because they interfere with nitrifying communities and stimulate N2O emission.
91 citations
Cites methods from "Conventional methanotrophs are resp..."
...Triplicate mRNA samples were pooled and used for library construction with the TruSeq RNASample PrepKit (Illumina) and sequenced on an IlluminaHiSeq4000 platform (70)....
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TL;DR: The results suggest that recent model projections may have overestimated CH4 emissions from rice agriculture and that CH4 emission estimates can be improved by considering the duration of straw incorporation and other management practices.
Abstract: Straw incorporation is a common long-term practice to improve soil fertility in croplands worldwide. However, straw amendments often increase methane (CH4) emissions from rice paddies, one of the main sources of anthropogenic CH4. Intergovernmental Panel on Climate Change (IPCC) methodologies to estimate CH4 emissions from rice agriculture assume that the effect of straw addition remains constant over time. Here, we show through a series of experiments and meta-analysis that these CH4 emissions acclimate. Effects of long-term (>5 years) straw application on CH4 emissions were, on average, 48% lower than IPCC estimates. Long-term straw incorporation increased soil methanotrophic abundance and rice root size, suggesting an increase in CH4 oxidation rates through improved O2 transport into the rhizosphere. Our results suggest that recent model projections may have overestimated CH4 emissions from rice agriculture and that CH4 emission estimates can be improved by considering the duration of straw incorporation and other management practices.
88 citations
References
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TL;DR: Version 4 of MEGA software expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses.
Abstract: We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at (http://www.megasoftware.net).
29,021 citations
University of Massachusetts Amherst1, University of Michigan2, University of New Mexico3, University of British Columbia4, Texas A&M University5, University of Minnesota6, University of Warwick7, Dalhousie University8, Colorado School of Mines9, University of Ljubljana10, Graz University of Technology11, Louisiana State University12
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
17,350 citations
TL;DR: The RDP Classifier can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes, and the majority of the classification errors appear to be due to anomalies in the current taxonomies.
Abstract: The Ribosomal Database Project (RDP) Classifier, a naive Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (≥95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
16,048 citations
TL;DR: UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences, and in testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus.
Abstract: Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments.
Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer.
Contact: [email protected]
Availability: Source, binaries and data: http://drive5.com/uchime.
Supplementary information:Supplementary data are available at Bioinformatics online.
11,904 citations
01 Jan 2007
TL;DR: Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris.
Abstract: Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe
7,720 citations