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      Methane-cycling microbial communities from Amazon floodplains and upland forests respond differently to simulated climate change scenarios

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          Abstract

          Seasonal floodplains in the Amazon basin are important sources of methane (CH 4), while upland forests are known for their sink capacity. Climate change effects, including shifts in rainfall patterns and rising temperatures, may alter the functionality of soil microbial communities, leading to uncertain changes in CH 4 cycling dynamics. To investigate the microbial feedback under climate change scenarios, we performed a microcosm experiment using soils from two floodplains (i.e., Amazonas and Tapajós rivers) and one upland forest. We employed a two-factorial experimental design comprising flooding (with non-flooded control) and temperature (at 27 °C and 30 °C, representing a 3 °C increase) as variables. We assessed prokaryotic community dynamics over 30 days using 16S rRNA gene sequencing and qPCR. These data were integrated with chemical properties, CH 4 fluxes, and isotopic values and signatures. In the floodplains, temperature changes did not significantly affect the overall microbial composition and CH 4 fluxes. CH 4 emissions and uptake in response to flooding and non-flooding conditions, respectively, were observed in the floodplain soils. By contrast, in the upland forest, the higher temperature caused a sink-to-source shift under flooding conditions and reduced CH 4 sink capability under dry conditions. The upland soil microbial communities also changed in response to increased temperature, with a higher percentage of specialist microbes observed. Floodplains showed higher total and relative abundances of methanogenic and methanotrophic microbes compared to forest soils. Isotopic data from some flooded samples from the Amazonas river floodplain indicated CH 4 oxidation metabolism. This floodplain also showed a high relative abundance of aerobic and anaerobic CH 4 oxidizing Bacteria and Archaea. Taken together, our data indicate that CH 4 cycle dynamics and microbial communities in Amazonian floodplain and upland forest soils may respond differently to climate change effects. We also highlight the potential role of CH 4 oxidation pathways in mitigating CH 4 emissions in Amazonian floodplains.

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          The online version contains supplementary material available at 10.1186/s40793-024-00596-z.

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

            Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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              Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

              The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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                Author and article information

                Contributors
                jbgontijo@ucdavis.edu
                Journal
                Environ Microbiome
                Environ Microbiome
                Environmental Microbiome
                BioMed Central (London )
                2524-6372
                17 July 2024
                17 July 2024
                2024
                : 19
                : 48
                Affiliations
                [1 ]Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, ( https://ror.org/036rp1748) Piracicaba, SP Brazil
                [2 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Department of Land, Air and Water Resources, , University of California, ; Davis, CA USA
                [3 ]Department of Biology, Stanford University, ( https://ror.org/00f54p054) Stanford, CA USA
                [4 ]Instituto de Formação Interdisciplinar e Intercultural, Universidade Federal do Oeste do Pará, ( https://ror.org/04603xj85) Santarém, PA Brazil
                [5 ]GRID grid.266683.f, ISNI 0000 0001 2166 5835, Department of Microbiology, , University of Massachusetts, ; Amherst, MA USA
                [6 ]GRID grid.170202.6, ISNI 0000 0004 1936 8008, Department of Biology, Institute of Ecology and Evolution, , University of Oregon, ; Eugene, OR USA
                [7 ]Netherlands Institute of Ecology, NIOO-KNAW, ( https://ror.org/01g25jp36) Wageningen, GE The Netherlands
                [8 ]Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, ( https://ror.org/02jbv0t02) Berkeley, CA USA
                Author information
                http://orcid.org/0000-0003-1942-7242
                http://orcid.org/0000-0002-0010-1552
                http://orcid.org/0000-0003-0438-4673
                http://orcid.org/0000-0002-8232-3086
                http://orcid.org/0000-0003-0427-7288
                http://orcid.org/0000-0003-3454-0702
                http://orcid.org/0000-0002-5901-1658
                http://orcid.org/0000-0001-7847-1724
                http://orcid.org/0000-0003-0980-7006
                http://orcid.org/0000-0003-4962-8870
                http://orcid.org/0000-0001-6769-5570
                http://orcid.org/0000-0002-0663-4448
                http://orcid.org/0000-0003-2907-1016
                http://orcid.org/0000-0002-5757-5572
                http://orcid.org/0000-0002-6446-6462
                http://orcid.org/0000-0002-3733-6312
                Article
                596
                10.1186/s40793-024-00596-z
                11256501
                39020395
                47ff9de9-fedd-469e-9fa1-1951f16cb61f
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 8 April 2024
                : 10 July 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: 2018/14974-0
                Award ID: 2014/50320-4
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002322, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior;
                Award ID: Finance Code 001
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 311008/2016-0
                Award Recipient :
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                Research
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                © BioMed Central Ltd., part of Springer Nature 2024

                amazon rainforest,wetlands,global warming,16s rrna sequencing,qpcr,methanogens,methanotrophs

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