Evaluation of long-read 16S rRNA next-generation sequencing for identification of bacterial isolates in a clinical diagnostic laboratory

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Abstract
Sanger sequencing of the first ~500 bp of the 16S rRNA gene is frequently used to identify bacterial pathogens that have ambiguous biochemical profiles or proteomic mass spectra. When diversity does not occur within that region, genus-level and/or species-level identification may not be possible, and a longer sequence or alternative target may be required to distinguish between genera/species. In this study, we evaluated a clinically relevant end-to-end solution for long-read (~1,500 nt) 16S rRNA next-generation sequencing by Oxford Nanopore Technologies (ONT) compared to a ~500 nt Sanger sequencing approach for the identification of 153 bacterial clinical isolates. Sequencing data were analyzed using the IDNS software from SmartGene and its proprietary 16S Centroid reference database (Centroid database) SmartGene software and the Centroid database. The agreement of the two platforms on species- and genus-level identification was determined, and discrepancies were resolved by whole-genome sequencing. ONT had a higher taxonomic resolution at the genus level ( P < 0.01). When genus-level identification was achieved by both methods, concordance to the best matching genus was 100%. When species-level identification was achieved by both methods, concordance to the best matching species was 91%. The costs per test were ~$25.30 (when multiplexing 24 samples/run) and $74 for ONT and Sanger sequencing, respectively. The hands-on time spent performing sequencing was similar for both methods, but the turnaround time of ONT was significantly shorter than that of Sanger sequencing. IMPORTANCE: This study adds to existing literature by describing a validated end-to-end solution of 16S rRNA gene Oxford Nanopore sequencing for bacterial isolate identification, including sequencing run time evaluation, automated analysis (SmartGene 16S Identification App) and interpretation of results, that can be incorporated into clinical and public health laboratories with a simple and cost-effective workflow.

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