16S rRNA gene sequencing on a benchtop sequencer: accuracy for identification of clinically important bacteria |
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| Authors: | GS Watts K Youens‐Clark MJ Slepian DM Wolk MM Oshiro GS Metzger D Dhingra LD Cranmer BL Hurwitz |
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| Institution: | 1. The University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA;2. Department of Pharmacology, University of Arizona, Tucson, AZ, USA;3. Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ, USA;4. Department of Medicine, University of Arizona, Tucson, AZ, USA;5. Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA;6. Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ, USA;7. Pharmaceutical Sciences, Geisinger Health System, Danville, PA, USA;8. Center for Infectious Disease Diagnostics and Research, Wilkes University, Danville, PA, USA;9. Life Technologies, Thermo Fisher Scientific, Carlsbad, CA, USA;10. School of Medicine, Seattle, WA, USA;11. University of Washington, Fred Hutchinson Cancer Research Center and Seattle Cancer Care Alliance, Seattle, WA, USA |
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| Abstract: | Aims Test the choice of 16S rRNA gene amplicon and data analysis method on the accuracy of identification of clinically important bacteria utilizing a benchtop sequencer. Methods and Results Nine 16S rRNA amplicons were tested on an Ion Torrent PGM to identify 41 strains of clinical importance. The V1–V2 region identified 40 of 41 isolates to the species level. Three data analysis methods were tested, finding that the Ribosomal Database Project's SequenceMatch outperformed BLAST and the Ion Reporter Metagenomics analysis pipeline. Lastly, 16S rRNA gene sequencing mixtures of four species through a six log range of dilution showed species were identifiable even when present as 0·1% of the mixture. Conclusions Sequencing the V1–V2 16S rRNA gene region, made possible by the increased read length Ion Torrent PGM sequencer's 400 base pair chemistry, may be a better choice over other commonly used regions for identifying clinically important bacteria. In addition, the SequenceMatch algorithm, freely available from the Ribosomal Database Project, is a good choice for matching filtered reads to organisms. Lastly, 16S rRNA gene sequencing's sensitivity to the presence of a bacterial species at 0·1% of a mixture suggests it has sufficient sensitivity for samples in which important bacteria may be rare. Significance and Impact of the Study We have validated 16S rRNA gene sequencing on a benchtop sequencer including simple mixtures of organisms; however, our results highlight deficits for clinical application in place of current identification methods. |
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| Keywords: | 16S rRNA gene accuracy bacterial identification benchtop sequencer sensitivity |
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