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      Multi-omics analyses provide insights into the sulfur metabolism of a novel deep-sea sulfate-reducing bacterium

      research-article
      1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 4 , 5 ,
      iScience
      Elsevier
      Microbial metabolism, Microbiology, Omics

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          Summary

          Sulfate-reducing bacteria (SRB) are ubiquitously distributed across various biospheres and play key roles in global sulfur and carbon cycles. However, few deep-sea SRB have been cultivated and studied in situ, limiting our understanding of the true metabolism of deep-sea SRB. Here, we firstly clarified the high abundance of SRB in deep-sea sediments and successfully isolated a sulfate-reducing bacterium (zrk46) from a cold seep sediment. Our genomic, physiological, and phylogenetic analyses indicate that strain zrk46 is a novel species, which we propose as Pseudodesulfovibrio serpens. We found that supplementation with sulfate, thiosulfate, or sulfite promoted strain zrk46 growth by facilitating energy production through the dissimilatory sulfate reduction, which was coupled to the oxidation of organic matter in both laboratory and deep-sea conditions. Moreover, in situ metatranscriptomic results confirmed that other deep-sea SRB also performed the dissimilatory sulfate reduction, strongly suggesting that SRB may play undocumented roles in deep-sea sulfur cycling.

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          Highlights

          • Clarified the high abundance of SRB in deep-sea cold seep sediments

          • Isolated a novel SRB from a deep-sea cold seep sediment

          • The novel SRB performed DSR coupled with organic matter oxidation

          • Deep-sea SRB indeed performed DSR in deep-sea environment

          Abstract

          Microbial metabolism; Microbiology; Omics

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          Most cited references63

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            FLASH: fast length adjustment of short reads to improve genome assemblies.

            Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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              UPARSE: highly accurate OTU sequences from microbial amplicon reads.

              Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                23 May 2024
                21 June 2024
                23 May 2024
                : 27
                : 6
                : 110095
                Affiliations
                [1 ]CAS and Shandong Province Key Laboratory of Experimental Marine Biology & Center of Deep Sea Research, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
                [2 ]Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
                [3 ]Center of Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
                [4 ]College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China
                Author notes
                []Corresponding author sunchaomin@ 123456qdio.ac.cn
                [5]

                Lead contact

                Article
                S2589-0042(24)01320-8 110095
                10.1016/j.isci.2024.110095
                11214288
                f90bba70-c983-473a-856f-c9ad9880ef2d
                © 2024 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 January 2024
                : 2 April 2024
                : 21 May 2024
                Categories
                Article

                microbial metabolism,microbiology,omics
                microbial metabolism, microbiology, omics

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