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16S rRNA Gene Amplicon Sequencing Data of Tailing and Nontailing Rhizosphere Soils of Mimosa pudica from a Heavy Metal-Contaminated Ex-Tin Mining Area.

ABSTRACT: The 16S rRNA gene amplicon sequence data from tailing and nontailing rhizosphere soils of Mimosa pudica from a heavy metal-contaminated area are reported here. Diverse bacterial taxa were represented in the results, and the most dominant phyla were Proteobacteria (41.2%), Acidobacteria (17.1%), and Actinobacteria (14.4%).

SUBMITTER: Abdullahi S

PROVIDER: S-EPMC7561685 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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16S rRNA Gene Amplicon Sequencing Data of Tailing and Nontailing Rhizosphere Soils of Mimosa pudica from a Heavy Metal-Contaminated Ex-Tin Mining Area.

Abdullahi Saidu S Haris Hazzeman H Zarkasi Kamarul Z KZ Amir Hamzah G HG

Microbiology resource announcements 20201015 42

The 16S rRNA gene amplicon sequence data from tailing and nontailing rhizosphere soils of Mimosa pudica from a heavy metal-contaminated area are reported here. Diverse bacterial taxa were represented in the results, and the most dominant phyla were Proteobacteria (41.2%), Acidobacteria (17.1%), and Actinobacteria (14.4%). ...[more]

PMID: 33060266

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Project description:Arecanut (Areca catechu L.) is an important plantation crop cultivated predominantly in the Indian states of Karnataka, Kerala, Assam, West Bengal, and Maharashtra in an area of 5.19 lakh ha, with Karnataka State alone accounting for about 68.41% of the area and 79.97% of production. Arecanut production has recently been hampered due to environmental and disease pressures, especially the escalating incidence of Yellow Leaf Disease (YLD). The involvement of phytoplasma as the etiological agent of YLD has been reported. Symptoms include yellowing at the tip of leaflets of two or three fronds of the outer most whorl which gradually spreads to the inner whorl of leaves. As the disease progresses, the entire crown becomes yellow leaving only the spear leaf green. In severe cases, the affected leaves often show necrosis from their tips. In advanced stages, the leaves are reduced in size and become stiff and pointed and the crown ultimately falls off. Degeneration of cortex is commonly observed in the diseased roots. The kernel of affected nuts shows discolouration and later turns blackish. The reduction in yield over a period of three years, immediately after the incidence of the disease, has been estimated to be around 50%. Harnessing the arecanut-microbiome interactions to address the biotic and abiotic stresses of the host plant offers immense opportunity to increase arecanut production sustainably. Here, we report a comprehensive analysis of the structural composition of the arecanut rhizosphere bacterial diversity utilizing next-generation sequencing (NGS) technology. We have used amplicon sequencing (V3-V4 regions of the 16S rRNA gene) of bulk soil and rhizosphere samples collected from YLD endemic regions of Aranthodu, Sullia Taluk, Dakshina Kannada District, Karnataka State, India, to assess the microbial diversity. The results revealed that while there is a great diversity of bacterial communities, relatively few bacterial phyla predominate with higher relative abundance. The phyla viz., Proteobacteria, Bacteroidetes, Firmicutes, Acidobacteria, Planctomycetes, Patescibacteria, Chloroflexi, Actinobacteria, Fusobacteria, and Verrucomicrobia were found to be dominant in the rhizosphere of the arecanut.

Project description:IntroductionBiological communities present in soil are essential to sustainable and productive agricultural practices; however, an accurate determination of the ecological status of agricultural soils remains to date an elusive task. An ideal indicator should be pervasive, play a relevant role in the ecosystem, show a rapid and proportional answer to external perturbations and be easily and economically measurable. Rhizobacteria play a major role in determining soil properties, becoming an attractive candidate for the detection of ecological indicators. The application of massive sequencing technologies to metagenomic analysis is providing an increasingly more precise view of the structure and composition of soil communities. In this work, we analyse soil rhizobacterial composition under various stress levels to search for potential ecological indicators.General biodiversity indicatorsOur results suggest that the Shannon index requires observation of a relatively large number of individuals to be representative of the true population diversity, and that the Simpson index may underestimate rare taxa in rhizobacterial environments.Taxonomical classification methodsDetection of indicator taxa requires comparison of taxonomical classification of sequences. We have compared RDP classifier, RTAX and similarity-based taxonomical classification and selected the latter for taxonomical assignment because it provides larger detail.Taxonomy-based ecological indicatorsThe study of significant variations in common, clearly identified, taxa, using paired datasets allows minimization of non-treatment effects and avoidance of false positives. We have identified taxa associated to specific perturbations as well as taxa generally affected in treated soils. Changes in these taxa, or combinations of them, may be used as ecological indicators of soil health. The overall number and magnitude of changes detected in taxonomic groups does also increase with stress. These changes constitute an alternative indicator to measuring specific taxa, although their determination requires large sample sizes, better obtained by massive sequencing.SummaryThe main ecological indicators available are the Shannon index, OTU counts and estimators, overall detection of the number and proportion of changes, and changes of specific indicator taxa. Massive sequencing remains the most accurate tool to measure rhizobacterial ecological indicators. When massive sequencing is not an option, various cultivable taxonomic groups, such as specific groups in the Actinobacteria tree, are attractive as potential indicators of large disruptions to the rhizobiome.

Dataset Information

16S rRNA Gene Amplicon Sequencing Data of Tailing and Nontailing Rhizosphere Soils of Mimosa pudica from a Heavy Metal-Contaminated Ex-Tin Mining Area.

Publications

16S rRNA Gene Amplicon Sequencing Data of Tailing and Nontailing Rhizosphere Soils of Mimosa pudica from a Heavy Metal-Contaminated Ex-Tin Mining Area.

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