Dataset Information

MinION™ Nanopore Sequencing of Skin Microbiome 16S and 16S-23S rRNA Gene Amplicons.

ABSTRACT: Human skin microbiome dysbiosis can have clinical consequences. Characterizing taxonomic composition of bacterial communities associated with skin disorders is important for dermatological advancement in both diagnosis and novel treatments. This study aims to analyze and improve the accuracy of taxonomic classification of skin bacteria with MinION™ nanopore sequencing using a defined skin mock community and a skin microbiome sample. We compared the Oxford Nanopore Technologies recommended procedures and concluded that their protocols highly bias the relative abundance of certain skin microbiome genera, most notably a large overrepresentation of Staphylococcus and underrepresentation of Cutibacterium and Corynebacterium. We demonstrated that changes in the amplification protocols improved the accuracy of the taxonomic classification for these three main skin bacterial genera. This study shows that MinION™ nanopore could be an efficient technology for full-length 16S rRNA sequencing; however, the analytical advantage is strongly influenced by the methodologies. The suggested alternatives in the sample processing improved characterization of a complex skin microbiome community using MinION™ nanopore sequencing.

SUBMITTER: Rozas M

PROVIDER: S-EPMC8766866 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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MinION™ Nanopore Sequencing of Skin Microbiome 16S and 16S-23S rRNA Gene Amplicons.

Rozas Miquel M Brillet François F Callewaert Chris C Paetzold Bernhard B

Frontiers in cellular and infection microbiology 20220105

Human skin microbiome dysbiosis can have clinical consequences. Characterizing taxonomic composition of bacterial communities associated with skin disorders is important for dermatological advancement in both diagnosis and novel treatments. This study aims to analyze and improve the accuracy of taxonomic classification of skin bacteria with MinION™ nanopore sequencing using a defined skin mock community and a skin microbiome sample. We compared the Oxford Nanopore Technologies recommended proced ...[more]

PMID: 35071053

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Project description:Background: Profiling the microbiome of low-biomass samples is challenging for metagenomics since these samples are prone to contain DNA from other sources (e.g. host or environment). The usual approach is sequencing short regions of the 16S rRNA gene, which fails to assign taxonomy to genus and species level. To achieve an increased taxonomic resolution, we aim to develop long-amplicon PCR-based approaches using Nanopore sequencing. We assessed two different genetic markers: the full-length 16S rRNA (~1,500 bp) and the 16S-ITS-23S region from the rrn operon (4,300 bp). Methods: We sequenced a clinical isolate of Staphylococcus pseudintermedius, two mock communities and two pools of low-biomass samples (dog skin). Nanopore sequencing was performed on MinION™ using the 1D PCR barcoding kit. Sequences were pre-processed, and data were analyzed using EPI2ME or Minimap2 with rrn database. Consensus sequences of the 16S-ITS-23S genetic marker were obtained using canu. Results: The full-length 16S rRNA and the 16S-ITS-23S region of the rrn operon were used to retrieve the microbiota composition of the samples at the genus and species level. For the Staphylococcus pseudintermedius isolate, the amplicons were assigned to the correct bacterial species in ~98% of the cases with the16S-ITS-23S genetic marker, and in ~68%, with the 16S rRNA gene when using EPI2ME. Using mock communities, we found that the full-length 16S rRNA gene represented better the abundances of a microbial community; whereas, 16S-ITS-23S obtained better resolution at the species level. Finally, we characterized low-biomass skin microbiota samples and detected species with an environmental origin. Conclusions: Both full-length 16S rRNA and the 16S-ITS-23S of the rrn operon retrieved the microbiota composition of simple and complex microbial communities, even from the low-biomass samples such as dog skin. For an increased resolution at the species level, targeting the 16S-ITS-23S of the rrn operon would be the best choice.

Project description:The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3-V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3-V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3-V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3-V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3-V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the basecall accuracy of our MinION™ sequencing protocol (96.4%), the misassignment of reads between closely related taxa is to be expected. With basecall accuracy exceeding 99% for recent MinION™ releases, the increased species-level differentiating power promised by longer amplicons like the 16S-ITS-23S rRNA region, may soon be fully realized.

Project description:BackgroundAccurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.ResultsBased on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.ConclusionsThese findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Dataset Information

MinION™ Nanopore Sequencing of Skin Microbiome 16S and 16S-23S rRNA Gene Amplicons.

Publications

MinION™ Nanopore Sequencing of Skin Microbiome 16S and 16S-23S rRNA Gene Amplicons.

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