ITS and 16S rDNA metagenomic dataset of different soils from flax fields
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ABSTRACT:
SUBMITTER: Zhernova D
PROVIDER: S-EPMC10696428 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:Soil contamination with heavy metals is a widespread problem, especially prominent on grounds lying in the vicinity of mines, smelters, and other industrial facilities. Many such areas are located in Southern Poland; they are polluted mainly with Pb, Zn, Cd, or Cu, and locally also with Cr. As for now, little is known about most bacterial species thriving in such soils and even less about a core bacterial community--a set of taxa common to polluted soils. Therefore, we wanted to answer the question if such a set could be found in samples differing physicochemically and phytosociologically. To answer the question, we analyzed bacterial communities in three soil samples contaminated with Pb and Zn and two contaminated with Cr and lower levels of Pb and Zn. The communities were assessed with 16S rRNA gene fragments pyrosequencing. It was found that the samples differed significantly and Zn decreased both diversity and species richness at species and family levels, while plant species richness did not correlate with bacterial diversity. In spite of the differences between the samples, they shared many operational taxonomic units (OTUs) and it was possible to delineate the core microbiome of our sample set. The core set of OTUs comprised members of such taxa as Sphingomonas, Candidatus Solibacter, or Flexibacter showing that particular genera might be shared among sites ~40 km distant.
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Project description:BACKGROUND:Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. RESULTS:To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. CONCLUSIONS:Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.).
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Project description:Dairy cow mastitis is an important disease in the dairy industry. Different microbial species have been identified as causative agents in mastitis, and are traditionally diagnosed by bacterial culture. The objective of this study was to use metagenomic pyrosequencing of bacterial 16S rRNA genes to investigate bacterial DNA diversity in milk samples of mastitic and healthy dairy cows and compare the results with those obtained by classical bacterial culture. One hundred and thirty-six milk samples were collected from cows showing signs of mastitis and used for microbiological culture. Additionally, 20 milk samples were collected from healthy quarters. Bacterial DNA was isolated from the same milk samples and the 16S rRNA genes were individually amplified and pyrosequenced. Discriminant analysis showed that the groups of samples that were most clearly different from the rest and thus easily discriminated were the normal milk samples from healthy cows and those characterised by culture as Trueperella pyogenes and Streptococcus spp. The mastitis pathogens identified by culture were generally among the most frequent organisms detected by pyrosequencing, and in some cases (Escherichia coli, Klebsiella spp. and Streptococcus uberis mastitis) the single most prevalent microorganism. Trueperella pyogenes sequences were the second most prevalent sequences in mastitis cases diagnosed as Trueperella pyogenes by culture, Streptococcus dysgalactiae sequences were the second most prevalent sequences in mastitis cases diagnosed as Streptococcus dysgalactiae by culture, and Staphyloccocus aureus sequences were the third most prevalent in mastitis cases diagnosed as Staphylococcus aureus by culture. In samples that were aerobic culture negative, pyrosequencing identified DNA of bacteria that are known to cause mastitis, DNA of bacteria that are known pathogens but have so far not been associated with mastitis, and DNA of bacteria that are currently not known to be pathogens. A possible role of anaerobic pathogens in bovine mastitis is also suggested.
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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.
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