Dataset Information

A Modified Low-quantity RNA-Seq Method for Microbial Community and Diversity Analysis Using Small Subunit Ribosomal RNA.

ABSTRACT: We propose a modified RNA-Seq method for small subunit ribosomal RNA (SSU rRNA)-based microbial community analysis that depends on the direct ligation of a 5' adaptor to RNA before reverse-transcription. The method requires only a low-input quantity of RNA (10-100 ng) and does not require a DNA removal step. Using this method, we could obtain more 16S rRNA sequences of the same regions (variable regions V1-V2) without the interference of DNA in order to analyze OTU (operational taxonomic unit)-based microbial communities and diversity. The generated SSU rRNA sequences are also suitable for the coverage evaluation for bacterial universal primer 8F (Escherichia coli position 8 to 27), which is commonly used for bacterial 16S rRNA gene amplification. The modified RNA-Seq method will be useful to determine potentially active microbial community structures and diversity for various environmental samples, and will also be useful for identifying novel microbial taxa.

SUBMITTER: Yan YW

PROVIDER: S-EPMC8275259 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Publications

A Modified Low-quantity RNA-Seq Method for Microbial Community and Diversity Analysis Using Small Subunit Ribosomal RNA.

Yan Yong-Wei YW Zhu Ting T Zou Bin B Quan Zhe-Xue ZX

Bio-protocol 20180505 9

We propose a modified RNA-Seq method for small subunit ribosomal RNA (SSU rRNA)-based microbial community analysis that depends on the direct ligation of a 5' adaptor to RNA before reverse-transcription. The method requires only a low-input quantity of RNA (10-100 ng) and does not require a DNA removal step. Using this method, we could obtain more 16S rRNA sequences of the same regions (variable regions V1-V2) without the interference of DNA in order to analyze OTU (operational taxonomic unit)-b ...[more]

PMID: 34286038

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Project description:RNA-seq-based SSU (small subunit) rRNA (ribosomal RNA) analysis has provided a better understanding of potentially active microbial community within environments. However, for RNA-seq library construction, high quantities of purified RNA are typically required. We propose a modified RNA-seq method for SSU rRNA-based microbial community analysis that depends on the direct ligation of a 5' adaptor to RNA before reverse-transcription. The method requires only a low-input quantity of RNA (10-100 ng) and does not require a DNA removal step. The method was initially tested on three mock communities synthesized with enriched SSU rRNA of archaeal, bacterial and fungal isolates at different ratios, and was subsequently used for environmental samples of high or low biomass. For high-biomass salt-marsh sediments, enriched SSU rRNA and total nucleic acid-derived RNA-seq datasets revealed highly consistent community compositions for all of the SSU rRNA sequences, and as much as 46.4%-59.5% of 16S rRNA sequences were suitable for OTU (operational taxonomic unit)-based community and diversity analyses with complete coverage of V1-V2 regions. OTU-based community structures for the two datasets were also highly consistent with those determined by all of the 16S rRNA reads. For low-biomass samples, total nucleic acid-derived RNA-seq datasets were analyzed, and highly active bacterial taxa were also identified by the OTU-based method, notably including members of the previously underestimated genus Nitrospira and phylum Acidobacteria in tap water, members of the phylum Actinobacteria on a shower curtain, and members of the phylum Cyanobacteria on leaf surfaces. More than half of the bacterial 16S rRNA sequences covered the complete region of primer 8F, and non-coverage rates as high as 38.7% were obtained for phylum-unclassified sequences, providing many opportunities to identify novel bacterial taxa. This modified RNA-seq method will provide a better snapshot of diverse microbial communities, most notably by OTU-based analysis, even communities with low-biomass samples.

Project description:Most microbes in the natural environment are difficult to cultivate. Thus, culture-independent analysis for microbial community structure is important for the understanding of its ecological functions. An immense ribosomal RNA sequence collection is available from phylogenetic research on organisms in all domains. These sequences are available for use in genetic research. However, the amplicon-seq process using PCR requires the construction of a sequence library. Construction can introduce bias into quantitative analyses, and each domain of species needs its own primer set. Total RNA sequencing has the advantage of analyzing an entire microbial community, including bacteria, archea, and eukaryote, at once. Such analysis yields large amounts of ribosomal RNA sequences that can be used for analysis without PCR bias. Evaluation using total RNA-seq for quantitative analysis of microbial communities and comparison with amplicon-seq is still rare. In the present study, we developed a mapping-based total RNA-seq analysis to obtain quantitative information on microbial community structure and compared our results with ordinary amplicon-seq methods. We read total RNA sequences from a commercially available mock community (ATCC MSA-2003) and divided reads into small subunit ribosomal RNA (ssrRNA) origin reads and others, such as mRNA origin reads. We then mapped ssrRNA origin reads on annotated assembled contigs and obtained quantitative results under several analysis strategies. Removal of low complexity sequences, sorting ssrRNA with paired-in mode, and performing homology-based taxonomical assignments (BLAST+ or vsearch) showed superior outcomes to other strategies. Results with this approach showed a median relative abundance among ten mock community members of ~10%; ordinary amplicon-seq showed a much lower percentage. Thus, total RNA-seq can be a powerful tool for analyzing microbial community structure and is not limited to analyzing gene expression profiling of microbiomes.

Dataset Information

A Modified Low-quantity RNA-Seq Method for Microbial Community and Diversity Analysis Using Small Subunit Ribosomal RNA.

Publications

A Modified Low-quantity RNA-Seq Method for Microbial Community and Diversity Analysis Using Small Subunit Ribosomal RNA.

Similar Datasets

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