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      Influence of Peanut, Sorghum, and Soil Salinity on Microbial Community Composition in Interspecific Interaction Zone

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          Abstract

          Soil microorganisms play important roles in crop production and sustainable agricultural management. However, soil conditions and crop selection are key determining factors for soil microbial communities. This study investigated the effect of plant types and soil salinity on the microbial community of interspecific interaction zone (II) based on the sorghum/peanut intercropping system. Microbial community diversity and composition were determined through PacBio single molecule, real-time sequencing of 16S rDNA and internal transcribed spacer (ITS) genes. Results showed Proteobacteria, Bacteroidota, and Acidobacteriota to be the dominant bacterial phyla in IP, II, and IS, whereas Ascomycota, Basidiomycota, and Mucoromycota were the dominant fungal phyla. Under salt-treated soil conditions, the plants-specific response altered the composition of the microbial community (diversity and abundance). Additionally, the interspecific interactions were also helpful for maintaining the stability and ecological functions of microbial communities by restructuring the otherwise stable core microbiome. The phylogenetic structure of the bacterial community was greatly similar between IP and II while that of the fungal community was greatly similar between IP and IS; however, the phylogenetic distance between IP and IS increased remarkably upon salinity stress. Overall, salinity was a dominant factor shaping the microbial community structure, although plants could also shape the rhizosphere microenvironment by host specificity when subjected to environmental stresses. In particular, peanut still exerted a greater influence on the microbial community of the interaction zone than sorghum.

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          Most cited references53

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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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            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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              UCHIME improves sensitivity and speed of chimera detection

              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: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                24 May 2021
                2021
                : 12
                : 678250
                Affiliations
                College of Agronomy, Shenyang Agricultural University , Shenyang, China
                Author notes

                Edited by: Markus Puschenreiter, University of Natural Resources and Life Sciences Vienna, Austria

                Reviewed by: Salma Mukhtar, University of the Punjab, Pakistan; Jincai Ma, Jilin Unversity, China

                *Correspondence: Yufei Zhou, zhouyufei@ 123456syau.edu.cn

                This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2021.678250
                8180576
                feb87fe9-7f2a-4a28-9638-249265301923
                Copyright © 2021 Shi, Zhao, Ren, Dong, Zhang, Dong, Jiang, Zhong, Zhou and Yu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 March 2021
                : 30 April 2021
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 53, Pages: 13, Words: 0
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                bacteria,fungi,mixed cultivation,phylum,rhizosphere,soil salinity
                Microbiology & Virology
                bacteria, fungi, mixed cultivation, phylum, rhizosphere, soil salinity

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