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.

Keywords: 16S; canine; dog; low-biomass; microbiome; microbiota; nanopore; rrn operon; skin.

Figure 1.. Violin plot representing the distribution of AS score for S. pseudintermedius isolate.

Alignment scores for each genetic marker were obtained using Minimap2 and either rrn DB or rrn DB with a reference added for S. pseudintermedius. The first two plots are for 16S-ITS-23S, whereas the second ones are for 16S rRNA.

Figure 2.. Assessment of the mapping-based strategy using a sample of Zymobiomics mock community.

Top part, histograms of the read counts distributed by alignment block depending on the database used (complete rrn DB vs rrn DB without Gammaproteobacteria). Alignment threshold at 3,000 bp (for 16S-ITS-23S) and 1,000 (for 16S rRNA gene) are marked with a red dashed line. Below part, histograms of the read counts distributed by AS scores depending on the database used (complete rrn DB vs rrn DB without Gammaproteobacteria).

Figure 3.. HM-783D mock community analysis.

( A) Heat map representing the HM-783D mock community composition when mapped to its mock database. Grey colour represents the bacteria that were not detected (<10 ⁴ copies with rrn operon). ( B) Linear regression analysis of relative read proportions obtained using full-length 16S rRNA gene for all bacterial species present in HM-783D mock community and the actual operon copies (in log scale). ( C) Linear regression analysis of relative read proportions obtained using the 16S-ITS-23S genetic marker for all bacterial species present in HM-783D mock community and the actual operon copies (in log scale).

Figure 4.. Zymobiomics mock community taxonomic analysis and diversity.

( A) Heat map representing the relative abundance of the Zymobiomics mock community. “REF” column represents the theoretical composition of the mock community regarding the 16S rRNA gene content of each bacterium. ( B) Alpha diversity rarefaction plot using observed taxa metrics.

Figure 5.. Microbiota composition of complex communities: skin samples of healthy dogs.

( A) Chin samples: heat map representing the relative abundance of the main bacterial species in chin samples using WIMP and Minimap2. ( B) Dorsal skin samples: heat map representing the relative abundance of the main bacterial species using WIMP and Minimap2. The lower heat map represents the remaining contamination from the previous run using HM-783D mock community within the same flowcell. Samples marked with a red line shared barcode with the mock community.