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      Microbiomes in the Challenger Deep slope and bottom-axis sediments

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          Abstract

          Hadal trenches are the deepest and most remote regions of the ocean. The 11-kilometer deep Challenger Deep is the least explored due to the technical challenges of sampling hadal depths. It receives organic matter and heavy metals from the overlying water column that accumulate differently across its V-shaped topography. Here, we collected sediments across the slope and bottom-axis of the Challenger Deep that enable insights into its in situ microbial communities. Analyses of 586 metagenome-assembled genomes retrieved from 37 metagenomes show distinct diversity and metabolic capacities between bottom-axis and slope sites. 26% of prokaryotic 16S rDNA reads in metagenomes were novel, with novelty increasing with water and sediment depths. These predominantly heterotrophic microbes can recycle macromolecules and utilize simple and complex hydrocarbons as carbon sources. Metagenome and metatranscriptome data support reduction and biotransformation of arsenate for energy gain in sediments that present a two-fold greater accumulation of arsenic compared to non-hadal sites. Complete pathways for anaerobic ammonia oxidation are predominantly identified in genomes recovered from bottom-axis sediments compared to slope sites. Our results expand knowledge of microbially-mediated elemental cycling in hadal sediments, and reveal differences in distribution of processes involved in nitrogen loss across the trench.

          Abstract

          The V-shaped Challenger Deep in the Mariana Trench is the deepest part of the world’s oceans. Using 586 prokaryotic metagenome-assembled genomes and metatranscriptomic data, this study explores metabolic capabilities and activities of microorganisms involved in elemental cycling in hadal sediments, and reveals the different distribution of processes between its bottom-axis and slope.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                wangyong@sz.tsinghua.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 March 2022
                21 March 2022
                2022
                : 13
                : 1515
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Institute of Deep-Sea Science and Engineering, , Chinese Academy of Sciences, ; Sanya, Hainan China
                [2 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, China
                [3 ]GRID grid.56466.37, ISNI 0000 0004 0504 7510, Department of Geology and Geophysics, , Woods Hole Oceanographic Institution, ; Woods Hole, MA USA
                [4 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Present Address: Institute for Ocean Engineering, , Shenzhen International Graduate School, Tsinghua University, ; Shenzhen, China
                Author information
                http://orcid.org/0000-0002-3757-0029
                http://orcid.org/0000-0003-0072-0238
                http://orcid.org/0000-0001-6805-381X
                http://orcid.org/0000-0003-3595-3867
                Article
                29144
                10.1038/s41467-022-29144-4
                8938466
                35314706
                5e5144d1-c544-40d9-8bfd-7b7ce9bab504
                © The Author(s) 2022

                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
                : 11 January 2021
                : 1 March 2022
                Funding
                Funded by: Strategic Priority Research Program B of Chinese Academy of Sciences (No. XDB06010201)
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                environmental microbiology,marine biology,microbial ecology
                Uncategorized
                environmental microbiology, marine biology, microbial ecology

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