Microbiota profiling with long amplicons using Nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon

Abstract

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.

Long-read sequencing to interrogate strain-level variation among adherent-invasiveEscherichia coliisolated from human intestinal tissue

“…Emerging singlemolecule (third-generation/long-read) sequencing technologies including Pacific Biosciences ("PacBio") and Oxford Nanopore Technologies ("nanopore") produce reads 10s to 100s of kilobases in length, often without prior amplification. Recent work [14][15][16][17] has demonstrated the utility of these longer reads for sequencing the entire 16S gene or entire rRNA operon, with corresponding increase in taxonomic resolution by capturing more variable sequence, including all nine variable regions of 16S, the internal transcribed spacers (ITS), and 23S. To support the extension of these approaches to characterize strain-level variation of tissue-associated (mucosaassociated) microbiota contributing to IBD and in models of experimental colitis in mice, with a particular focus on mechanistic studies of AIEC, we produced complete genome assemblies for eight AIEC and non-AIEC E. coli, describe the genomic variation among these strains, particularly within the rRNA operon, and demonstrate the accurate identification of these strains in mixed in vitro and in vivo microbiota.…”

A lab in the field: applications of real-time, in situ metagenomic sequencing

“…These results were consistent with observations from [ 61 ], which showed that the bacterial identification at the genus level was reliable. Species-level missclassifications could be partially addressed by employing different—and optimized—bioinformatic approaches for the taxonomic classification [ 45 , 62 ], by sequencing the complete 16S-ITS-23S region of the ribosomal operon [ 63 , 64 ], or by coupling MinION sequencing with complementary quantitative PCR assays [ 60 ].…”

High precisionNeisseria gonorrhoeaevariant and antimicrobial resistance calling from metagenomic Nanopore sequencing

“…The long reads generated can allow taxonomic classification with greater specificity than is possible with short reads. 4 Additionally, as reads containing antimicrobial resistance determinants (with the exception of those on plasmids) contain greater amounts of genetic context than is found with short reads, assignment of resistance determinants to a species is more precise. However, ONT data contains a substantial per base error rate of up to 10% with assemblies containing open reading frame disrupting insertion or deletion errors.…”