Species-level profiling of environmental microbiota with high-accuracy full-length nanopore 16S sequencing

Rapid bacterial detection plays a vital role in researching microbial ecology. Understanding bacterial composition, diversity, and dynamics is key in the response to pathogen emergence and outbreaks. Sequencing of the 16S ribosomal RNA (rRNA) gene is a predominant method for microbial identification and has applications in microbiome characterisation, food safety, clinical microbiology, and environmental monitoring.

The bacterial 16S rRNA gene comprises nine hypervariable regions (V1 to V9), which exhibit sequence differences among bacterial species, interspersed by highly conserved sequences. Traditionally, PCR amplification of the V3–V4 region followed by short-read sequencing has been used to classify and identify bacteria at various taxonomic levels. However, short reads cannot cover the full length of the 16S rRNA gene (~1,500 bp), limiting the resolution for species-level identification.

Zhang et al. assessed the effectiveness of the latest nanopore sequencing chemistry to generate long reads for full-length 16S sequencing1. The team used a PromethION device from Oxford Nanopore Technologies to sequence 16S amplicons from both mock microbial communities and environmental samples. Post-filtering, this generated a mean of over 57,000 reads per sample, with an average Q score of 22.41 (accuracy: 99.42%). The similarity of 16S sequences impacts microbial species identification; high-accuracy nanopore sequencing therefore allowed the researchers to successfully profile mock communities at the species level.

One of the mock community samples was constructed to be intentionally challenging, containing several microbes of the same genus but different species with similar 16S sequences. At the species level, the long reads generated by nanopore sequencing ‘obtained complete [species] recall (12/12)’. Conversely, sequencing of the same mock community sample using a short-read sequencing technology failed to achieve species-level identification.

Mock communities can be useful for evaluating the effectiveness of sequencing platforms, but they lack richness and do not represent the real world. To address this, Zhang et al. also used high-accuracy full-length nanopore 16S sequencing to analyse the microbial compositions of real environmental samples from water and soil. Comparing nanopore sequencing to an alternative long-read sequencing platform revealed that the technologies produced similar microbial profiles for the most abundant species in each sample; however, nanopore sequencing ‘identified more species [overall] in environmental data’. This higher level of species detection makes nanopore sequencing more likely to identify uncommon or low-abundance species, which can have valuable applications in various fields such as biotechnology. Uncommon bacteria may possess unique biochemical or genetic characteristics that could be utilised in bioremediation, pharmaceutical development, or industrial processes.

‘Our findings show that the [Oxford Nanopore Technologies] R10.4.1 flowcell for full-length 16S enabled species-level taxonomic identification for environmental samples’1.

Concluding their research, Zhang et al. summarised that full-length nanopore 16S sequencing can accurately resolve microbial community profiling at the species level, and that the team ‘hope that in the future … the [long-read] amplicons will replace the short-read amplicons that are currently being used and revolutionize the field’. In addition, the authors emphasised the lower cost of nanopore sequencing per 10,000 reads than the alternative long-read platform used in the study and stated: ‘Considering the advantages of high throughput and portability of [Oxford Nanopore Technologies] sequencing, we believe that more researchers will employ [Oxford Nanopore Technologies] sequencing to explore environmental microbes’.

  1. Zhang, T. et al. The newest Oxford Nanopore R10.4.1 full-length 16S rRNA sequencing enables the accurate resolution of species-level microbial community profiling. Appl. Environ. Microbiol. 89(10):e00605-23 (2023). DOI: https://doi.org/10.1128/aem.00605-23