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Clinical Trial
. 2014 May 20;9(5):e97699.
doi: 10.1371/journal.pone.0097699. eCollection 2014.

Microbial community profiling of human saliva using shotgun metagenomic sequencing

Nur A Hasan  1 , Brian A Young  2 , Angela T Minard-Smith  2 , Kelly Saeed  3 , Huai Li  3 , Esley M Heizer  2 , Nancy J McMillan  2 , Richard Isom  3 , Abdul Shakur Abdullah  3 , Daniel M Bornman  2 , Seth A Faith  2 , Seon Young Choi  1 , Michael L Dickens  2 , Thomas A Cebula  4 , Rita R Colwell  5
Affiliations
Clinical Trial

Microbial community profiling of human saliva using shotgun metagenomic sequencing

Nur A Hasan et al. PLoS One. .

Erratum in

  • PLoS One. 2014;9(8):e106124

Abstract

Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUS and BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

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Conflict of interest statement

Competing Interests: Rita R. Colwell is Founder and Chairman of the Board, CosmosID®, and some of the other authors are employees of the company, a bioinformatics company. Rita R. Colwell is also Distinguished University Professor at the University of Maryland, College Park and at Johns Hopkins University Bloomberg School of Public Health. Affiliation with CosmosID and, similarly, with Battelle does not alter the authors’ adherence to all PLoS One policies on sharing data and materials online as detailed in the online guide for authors.

Figures

Figure 1
Figure 1. Screenshot of GENIUS client software displaying tabular output of in silico metagenomic data.
Left panel indicates projects loaded to this graphical user interface. Three table views to the rightmost panel represent output of 5VCE (a)NmerCE (b) and merged output from both 5VCE and NmerCE (c) algorithms, respectively.
Figure 2
Figure 2. Genus overlap for sample VFD10-018 (a) and VFD12-006 (b) estimated by 16S sequencing/NCBI 16S BLAST and GAIIx sequencing/5VCE-NmerCE.
Figure 3
Figure 3. Relative abundance of species in VFD10-018 estimated by GAIIx sequencing and BLAST (microbial reference database), 5VCE, and NmerCE algorithms.
Figure 4
Figure 4. Relative abundance of species in VFD12-006 estimated by GAIIx sequencing and BLAST (microbial reference database), 5VCE, and NmerCE algorithms.
Figure 5
Figure 5. Principal component analysis of data for seven saliva samples analyzed by GENIUS.
Figure 6
Figure 6. Double hierarchical dendrogram showing bacterial distribution at the species level for seven saliva samples.
The relative values for bacterial species are depicted by color intensity, with legend indicated at the top of the figure.
See this image and copyright information in PMC

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