No retraction, correction or concern found when last checked on Oct 24, 2026.
Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes
Joshua P. Earl, Nithin D. Adappa, Jaroslaw Krol and 11 more
Microbiome · 2018 · CC BY
Abstract
Background. Pan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences’ (PacBio’s) long-read technology in particular suffers from high error rates per base. Herein, we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data.
Results. Analysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity with regard to taxonomic classification. Examination of a 250-plus species mock community demonstrated correct species-level classification of > 90% of taxa, and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes) was the predominant species throughout, but was found at distinct relative abundances by site.
Conclusions. Our microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt) overcomes deficits of standard marker gene-based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.
Electronic supplementary material. The online version of this article (10.1186/s40168-018-0569-2) contains supplementary material, which is available to authorized users.
References 109
- 1.
Woese CR. Bacterial evolution. Microbiol Rev. 1987;51(2):221-271.
Not in our library10.1128/mr.51.2.221-271.1987
- 2.
Woese CR, Fox GE. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc Natl Acad Sci U S A. 1977;74(11):5088-5090.
Not in our library10.1073/pnas.74.11.5088
- 3.
Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci U S A. 1985;82(20):6955-6959.
Not in our library10.1073/pnas.82.20.6955
- 4.
Olsen GJ, Woese CR. Ribosomal RNA: a key to phylogeny. FASEB J. 1993;7(1):113-123.
Not in our library10.1096/fasebj.7.1.8422957
- 5.
Welch DBM, Mark Welch DB, Huse SM. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Handbook of molecular microbial ecology II. 2011;243-252.
Not in our library10.1073/pnas.0605127103
- 6.
Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173(2):697-703.
Not in our library10.1128/jb.173.2.697-703.1991
- 7.
Terlizzi A, Anderson MJ, Bevilacqua S, Fraschetti S, Włodarska-Kowalczuk M, Ellingsen KE. Beta diversity and taxonomic sufficiency: do higher-level taxa reflect heterogeneity in species composition? Divers Distrib. 2009;15(3):450-458.
Not in our library10.1111/j.1472-4642.2008.00551.x
- 8.
Anderson MJ, Ellingsen KE, McArdle BH. Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006;9(6):683-693.
Not in our library10.1111/j.1461-0248.2006.00926.x
- 9.
- 10.
- 11.
Kim M, Morrison M, Yu Z. Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes. J Microbiol Methods. 2011;84(1):81-87.
Not in our library10.1016/j.mimet.2010.10.020
- 12.
Di Bella JM, Bao Y, Gloor GB, Burton JP, Reid G. High throughput sequencing methods and analysis for microbiome research. J Microbiol Methods. 2013;95(3):401-414.
Not in our library10.1016/j.mimet.2013.08.011
- 13.
Liu Z, DeSantis TZ, Andersen GL, Knight R. Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucleic Acids Res. 2008;36(18):e120.
Not in our library10.1093/nar/gkn491
- 14.
Youssef N, Sheik CS, Krumholz LR, Najar FZ, Roe BA, Elshahed MS. Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys. Appl Environ Microbiol. 2009;75(16):5227-5236.
Not in our library10.1128/aem.00592-09
- 15.
- 16.
Teeling H, Glöckner FO. Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective. Brief Bioinform. 2012;13(6):728-742.
Not in our library10.1093/bib/bbs039
- 17.
Wang J, Jia H. Metagenome-wide association studies: fine-mining the microbiome. Nat Rev Microbiol. 2016;14(8):508-522.
Not in our library10.1038/nrmicro.2016.83
- 18.
- 19.
Mosher JJ, Bernberg EL, Shevchenko O, Kan J, Kaplan LA. Efficacy of a 3rd generation high-throughput sequencing platform for analyses of 16S rRNA genes from environmental samples. J Microbiol Methods. 2013;95(2):175-181.
Not in our library10.1016/j.mimet.2013.08.009
- 20.
Babauta JT, Atci E, Ha PT, Lindemann SR, Ewing T, Call DR, Fredrickson JK, Beyenal H. Localized electron transfer rates and microelectrode-based enrichment of microbial communities within a phototrophic microbial mat. Front Microbiol. 2014;5:11.
Not in our library10.3389/fmicb.2014.00011
- 21.
Mosher JJ, Bowman B, Bernberg EL, Shevchenko O, Kan J, Korlach J, Kaplan LA. Improved performance of the PacBio SMRT technology for 16S rDNA sequencing. J Microbiol Methods. 2014;104:59-60.
Not in our library10.1016/j.mimet.2014.06.012
- 22.
- 23.
- 24.
Singer E, Bushnell B, Coleman-Derr D, Bowman B, Bowers RM, Levy A, Gies EA, Cheng JF, Copeland A, Klenk HP, et al. High-resolution phylogenetic microbial community profiling. ISME J. 2016;10(8):2020-2032.
Not in our library10.1038/ismej.2015.249
- 25.
- 26.
Abreu NA, Nagalingam NA, Song Y, Roediger FC, Pletcher SD, Goldberg AN, Lynch SV. Sinus microbiome diversity depletion and Corynebacterium tuberculostearicum enrichment mediates rhinosinusitis. Sci Transl Med. 2012;4(151):151ra124.
Not in our library10.1126/scitranslmed.3003783
- 27.
Aurora R, Chatterjee D, Hentzleman J, Prasad G, Sindwani R, Sanford T. Contrasting the microbiomes from healthy volunteers and patients with chronic rhinosinusitis. JAMA Otolaryngol Head Neck Surg. 2013;139(12):1328-1338.
Not in our library10.1001/jamaoto.2013.5465
- 28.
Stephenson MF, Mfuna L, Dowd SE, Wolcott RD, Barbeau J, Poisson M, James G, Desrosiers M. Molecular characterization of the polymicrobial flora in chronic rhinosinusitis. J Otolaryngol Head Neck Surg. 2010;39(2):182-187.
Not in our library
- 29.
Stressmann FA, Rogers GB, Chan SW, Howarth PH, Harries PG, Bruce KD, Salib RJ. Characterization of bacterial community diversity in chronic rhinosinusitis infections using novel culture-independent techniques. Am J Rhinol Allergy. 2011;25(4):e133-e140.
Not in our library10.2500/ajra.2011.25.3628
- 30.
- 31.
Lee JT, Frank DN, Ramakrishnan V. Microbiome of the paranasal sinuses: update and literature review. Am J Rhinol Allergy. 2016;30(1):3-16.
Not in our library10.2500/ajra.2016.30.4255
- 32.
Bezerra TF, Padua FG, Gebrim EM, Saldiva PH, Voegels RL. Biofilms in chronic rhinosinusitis with nasal polyps. Otolaryngol Head Neck Surg. 2011;144(4):612-616.
Not in our library10.1177/0194599811399536
- 33.
Feazel LM, Robertson CE, Ramakrishnan VR, Frank DN. Microbiome complexity and Staphylococcus aureus in chronic rhinosinusitis. Laryngoscope. 2012;122(2):467-472.
Not in our library10.1002/lary.22398
- 34.
- 35.
- 36.
- 37.
Paju S, Bernstein JM, Haase EM, Scannapieco FA. Molecular analysis of bacterial flora associated with chronically inflamed maxillary sinuses. J Med Microbiol. 2003;52(Pt 7):591-597.
Not in our library10.1099/jmm.0.05062-0
- 38.
Power DA, Burton JP, Chilcott CN, Tagg JR, Dawes PJ. Non-culture-based analysis of bacterial populations from patients with chronic rhinosinusitis. J Clin Microbiol. 2005;43(11):5822-5824.
Not in our library10.1128/jcm.43.11.5822-5824.2005
- 39.
Kaspar Ursula, Kriegeskorte André, Schubert Tanja, Peters Georg, Rudack Claudia, Pieper Dietmar H., Wos-Oxley Melissa, Becker Karsten. The culturome of the human nose habitats reveals individual bacterial fingerprint patterns. Environmental Microbiology. 2015;18(7):2130-2142.
Not in our library10.1111/1462-2920.12891
- 40.
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996-998.
Not in our library10.1038/nmeth.2604
Cited by papers we hold 4
- Current challenges and best-practice protocols for microbiome analysis · 2019No notice
- Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis · 2019No notice
- High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution · 2019No notice
- The microbiome of the upper respiratory tract in health and disease · 2019No notice
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.