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      Termite gas emissions select for hydrogenotrophic microbial communities in termite mounds

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          Abstract

          Organoheterotrophs are the dominant bacteria in most soils worldwide. While many of these bacteria can subsist on atmospheric hydrogen (H 2), levels of this gas are generally insufficient to sustain hydrogenotrophic growth. In contrast, bacteria residing within soil-derived termite mounds are exposed to high fluxes of H 2 due to fermentative production within termite guts. Here, we show through community, metagenomic, and biogeochemical profiling that termite emissions select for a community dominated by diverse hydrogenotrophic Actinobacteriota and Dormibacterota. Based on metagenomic short reads and derived genomes, uptake hydrogenase and chemosynthetic RuBisCO genes were significantly enriched in mounds compared to surrounding soils. In situ and ex situ measurements confirmed that high- and low-affinity H 2-oxidizing bacteria were highly active in the mounds, such that they efficiently consumed all termite-derived H 2 emissions and served as net sinks of atmospheric H 2. Concordant findings were observed across the mounds of three different Australian termite species, with termite activity strongly predicting H 2 oxidation rates ( R 2 = 0.82). Cell-specific power calculations confirmed the potential for hydrogenotrophic growth in the mounds with most termite activity. In contrast, while methane is produced at similar rates to H 2 by termites, mounds contained few methanotrophs and were net sources of methane. Altogether, these findings provide further evidence of a highly responsive terrestrial sink for H 2 but not methane and suggest H 2 availability shapes composition and activity of microbial communities. They also reveal a unique arthropod–bacteria interaction dependent on H 2 transfer between host-associated and free-living microbial communities.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fast and sensitive protein alignment using DIAMOND.

            The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.
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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

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                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                July 20 2021
                July 27 2021
                July 20 2021
                July 27 2021
                : 118
                : 30
                : e2102625118
                Article
                10.1073/pnas.2102625118
                34285074
                ded2e91c-aeb6-41ce-9c3f-29c9c0f46a15
                © 2021

                Free to read

                https://www.pnas.org/site/aboutpnas/licenses.xhtml

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