RRC ID 47733
Author Hoshino T, Inagaki F.
Title Application of Stochastic Labeling with Random-Sequence Barcodes for Simultaneous Quantification and Sequencing of Environmental 16S rRNA Genes.
Journal PLoS One
Abstract Next-generation sequencing (NGS) is a powerful tool for analyzing environmental DNA and provides the comprehensive molecular view of microbial communities. For obtaining the copy number of particular sequences in the NGS library, however, additional quantitative analysis as quantitative PCR (qPCR) or digital PCR (dPCR) is required. Furthermore, number of sequences in a sequence library does not always reflect the original copy number of a target gene because of biases caused by PCR amplification, making it difficult to convert the proportion of particular sequences in the NGS library to the copy number using the mass of input DNA. To address this issue, we applied stochastic labeling approach with random-tag sequences and developed a NGS-based quantification protocol, which enables simultaneous sequencing and quantification of the targeted DNA. This quantitative sequencing (qSeq) is initiated from single-primer extension (SPE) using a primer with random tag adjacent to the 5' end of target-specific sequence. During SPE, each DNA molecule is stochastically labeled with the random tag. Subsequently, first-round PCR is conducted, specifically targeting the SPE product, followed by second-round PCR to index for NGS. The number of random tags is only determined during the SPE step and is therefore not affected by the two rounds of PCR that may introduce amplification biases. In the case of 16S rRNA genes, after NGS sequencing and taxonomic classification, the absolute number of target phylotypes 16S rRNA gene can be estimated by Poisson statistics by counting random tags incorporated at the end of sequence. To test the feasibility of this approach, the 16S rRNA gene of Sulfolobus tokodaii was subjected to qSeq, which resulted in accurate quantification of 5.0 × 103 to 5.0 × 104 copies of the 16S rRNA gene. Furthermore, qSeq was applied to mock microbial communities and environmental samples, and the results were comparable to those obtained using digital PCR and relative abundance based on a standard sequence library. We demonstrated that the qSeq protocol proposed here is advantageous for providing less-biased absolute copy numbers of each target DNA with NGS sequencing at one time. By this new experiment scheme in microbial ecology, microbial community compositions can be explored in more quantitative manner, thus expanding our knowledge of microbial ecosystems in natural environments.
Volume 12(1)
Pages e0169431
Published 2017-1-4
DOI 10.1371/journal.pone.0169431
PII PONE-D-16-19558
PMID 28052139
PMC PMC5215600
MeSH Base Sequence DNA Barcoding, Taxonomic* Environment* Gene Dosage High-Throughput Nucleotide Sequencing / methods* RNA, Ribosomal, 16S / genetics* Stochastic Processes
IF 2.74
Times Cited 5
WOS Category MICROBIOLOGY
Resource
DNA material Genomic DNAs of Paracoccus denitrificansJCM 21484T (JGD12662) Sulfolobus tokodaii JCM 10545T (JGD07500) Halomonas elongata JCM 21044T (JGD08103) Methanocaldococcus jannaschii JCM 10045T (JGD12154) Bacillus subtilis subsp. subtilis JCM 1465T (JGD08099) Streptomyces avermitilis JCM 5070T (JGD12254).
General Microbes