Analysis of the salivary microbiome using culture-independent techniques
© Lazarevic et al; licensee BioMed Central Ltd. 2012
Received: 29 August 2011
Accepted: 2 February 2012
Published: 2 February 2012
The salivary microbiota is a potential diagnostic indicator of several diseases. Culture-independent techniques are required to study the salivary microbial community since many of its members have not been cultivated.
We explored the bacterial community composition in the saliva sample using metagenomic whole genome shotgun (WGS) sequencing, the extraction of 16S rRNA gene fragments from metagenomic sequences (16S-WGS) and high-throughput sequencing of PCR-amplified bacterial 16S rDNA gene (16S-HTS) regions V1 and V3.
The hierarchical clustering of data based on the relative abundance of bacterial genera revealed that distances between 16S-HTS datasets for V1 and V3 regions were greater than those obtained for the same V region with different numbers of PCR cycles. Datasets generated by 16S-HTS and 16S-WGS were even more distant. Finally, comparison of WGS and 16S-based datasets revealed the highest dissimilarity.
The analysis of the 16S-HTS, WGS and 16S-WGS datasets revealed 206, 56 and 39 bacterial genera, respectively, 124 of which have not been previously identified in salivary microbiomes. A large fraction of DNA extracted from saliva corresponded to human DNA. Based on sequence similarity search against completely sequenced genomes, bacterial and viral sequences represented 0.73% and 0.0036% of the salivary metagenome, respectively. Several sequence reads were identified as parts of the human herpesvirus 7.
Analysis of the salivary metagenome may have implications in diagnostics e.g. in detection of microorganisms and viruses without designing specific tests for each pathogen.
The microbiota in the mouth has a significant impact on both the oral and general health. Bacterial species associated with periodontal health and those that are more prevalent in periodontal disease have been identified . The salivary microbiota is a potential diagnostic indicator of several diseases. For instance, a caries-free oral status in children is associated with a significant shift in the relative abundance of Porphyromonas catoniae and Neisseria flavescens in saliva . Increased salivary counts of Capnocytophaga gingivalis, Prevotella melaninogenica and Streptococcus mitis are associated with oral cancer . The salivary level of the bacterium Selenomonas noxia correlates with obesity in women .
The study of the oral microbiota as well as its salivary component requires culture-independent techniques, since about one third of 700 bacterial species identified in the human oral cavity have not been cultivated . These may be based on PCR amplification and high-throughput sequencing of the bacterial 16S rRNA genes (16S-HTS) or the metagenomic whole genome shotgun (WGS) sequencing. The latter approach may include either the analysis of the totality of generated DNA fragments or of the 16S rRNA gene fragments retrieved from the metagenome (16S-WGS) . Both 16S-WGS and 16S-HTS approaches present limitations and advantages over each other .
Here we explored the microbial community composition in the saliva sample using WGS, 16S-WGS and 16S-HTS. In addition, to assess putative biases due to PCR amplification, we compared taxonomic composition of 16S-HTS datasets obtained after different number of PCR cycles.
The study was conducted according to the current version of Declaration of Helsinki and approved by the Ethics Committee of HUG (09-078). Unstimulated saliva was obtained with informed consent from a 32-year male smoker without obvious signs of oral disease. The sample was collected by spitting in a sterile plastic 50-mL tube at 10:30 a.m., 1.5 hours after eating. Six hundred μL saliva was mixed with the same volume of 2x lysis buffer [Tris 20 mM, EDTA 2 mM (pH 8), Tween 1%] and Proteinase K (Eurobio) 200 μg/mL. After a 2.5 hour incubation at 55°C, proteinase K was inactivated by a 10-min heating at 95°C. The saliva lysate was divided in six 200-μL aliquots to which RnaseA (Roche) 40 μg/mL was added. Samples were incubated for 5 min at room temperature. From that point, the DNeasy Blood & Tissue Kit (QIAGEN) was used following the manufacturer's Spin-Column Protocol for Purification of Total DNA from Animal Blood or Cells (DNeasy Blood & Tissue Handbook 07/2006). DNA was eluted using 110 μL of supplied AE Buffer, then the pooled eluate (metagenomic DNA) was concentrated to 80 ng/μL. Total DNA quantity was assessed using a NanoDrop ND-8000 spectrophotometer (NanoDrop Technologies).
PCR and sequencing
PCR amplification was carried out in a 50-μL PrimeStar HS Premix (Takara) containing 8 ng of purified DNA and 0.5 μM of each forward and reverse primer. The 16S rDNA V1-3 amplicons generated with primers 5'-GAGTTTGATCMTGGCTCAG (V1 forward) and 5'-CCGCGRCTGCTGGCAC (V3 reverse) corresponded to E. coli positions 28 to 514 after exclusion of primers sequences. The samples were run in four replicate PCRs for 20, 25 or 30 cycles using the following parameters: 98°C for 10 s, 60°C for 15 s, and 72°C for 1 min. The four replicate PCRs were then pooled.
Paired-end DNA libraries were prepared according to the manufacturer's (Illumina) instructions. Metagenomic DNA fragments of about 300 bp and 16S rDNA amplicons were barcoded using specific 6-base sequences. The libraries were sequenced from both ends for 100 cycles (excluding barcode sequences) on the Illumina Hi-Seq 2000 using TruSeq SBS v5 kit. A barcoded PhiX reference was spiked in the same channel to estimate the error rate.
Parameters of the initial quality filter were the following: (i) maximum one base below a quality of 5 in the first 70 bases; (ii) a minimum average quality of 10; (iii) no ambiguous base allowed. After filtering, the average Q30 was larger than 75% and the average PhiX error rate was 0.7%. Only pairs were retained in the filtered data, i.e. if one read was filtered out the paired read was removed. Each of the three 16S-HTS datasets (20, 25 and 30 PCR cycles) was reduced by randomly picking 1.2 million sequence read pairs. Then, in the second filtering step, we removed sequences containing incorrect PCR primer sequences or runs of ≥ 12 identical nucleotides. The WGS dataset was reduced to one million sequence pairs and was not subject to additional filtering steps. Sequences were deposited in MG-RAST under accession numbers 4477823.3, 4477824.3, 4477839.3, 4477840.3, 4478078.3, 4478079.3, 4478080.3, 4478370.3, 4478371.3, 4479520.3, 4479521.3, 4479522.3, 4479523.3 and 4479524.3.
The 16S rDNA sequences were clustered to operational taxonomic units (OTUs) defined at 95% identity using CD-HIT . The V1 and V3 sequences were assigned the taxonomic identity using the Ribosomal Database Project (RDP) Classifier  with a recommended 50% confidence cutoff. Taxonomic assignments of sequences from the WGS dataset were made using BLASTN  against NCBI prokaryotic, viral and fungal databases as well as against the human sequences from NCBI and EBI databases. The criteria used were a wordsize of 16, ≥ 94% identity, ≥ 90 overlap and e-value ≤10-30. The bacterial 16S rDNA sequences were extracted from the WGS dataset using CAMERA  and HMMER search option. They were then filtered using an e value ≤10-10 and assigned to genera using the RDP Classifier.
Group-average clustering of data was performed using a Bray-Curtis similarity matrix in PRIMER-E (Plymouth), based on square-root-transformed genera abundance.
Results and Discussion
Description of the 4 sequence datasets and 14 subsets
16S-HTS, 20 cycles
16S-HTS, 25 cycles
16S-HTS, 30 cycles
Randomly chosen read pairs
16S rDNA region subset
Filtered forward reads
Filtered reverse reads
Taxa abundance as a function of the PCR cycle number in the 16S-HTS datasets
There is a concern that short Illumina reads and sequence errors may compromise the quality of taxonomic assignments [16–18]. To assess the accuracy of taxonomic assignments we extracted 81-base V1 and 84-base V3 sequences from the 16Sr RNA gene for 660 species from the Human Oral Microbiome Database (HOMD)  for which the taxonomic information was available at the genus level. These simulated Illumina reads were assigned taxonomy using the RDP Classifier with a recommended 50% bootstrap cutoff. The proportion of V1 and V3 sequences correctly assigned at the genus level reached 68% and 76%, respectively (Additional File 2). For both, V1 and V3 regions, the accuracy of taxonomic assignment at the phylum level was greater than 95%.
We clustered sequence reads generated by Illumina sequencing into OTUs, defined at ≥ 95% identity, which roughly corresponds to genus-level grouping  and may have the effect of absorbing some sequence errors . Then, we compared the OTU content across datasets obtained after different number of PCR cycles using the phylum-level affiliation of representative OTUs derived from the RDP Classifier. The OTUs that met the criteria described in Additional file 3 were selected for comparisons. This approach confirmed the trend observed when taxonomy was assigned to each sequence read (see above); the relative abundance of the majority of OTUs from the phyla Actinobacteria and Firmicutes was decreased whereas the proportion of most OTUs from the phyla Bacteroidetes, Fusobacteria and Spirochaetes was enhanced by increasing the number of PCR cycles (Additional file 3).
Therefore, performing more PCR cycles, which may be required when little template DNA is available, may introduce amplification biases and increase the distance from samples for which less PCR cycles were performed.
Taxonomic assignment in the WGS datasets
Number of BLATSN hits against human, bacterial and viral databases
BLASTN hita counts
Comparison of the 16S-HTS and WGS datasets
Similarity between the V3 16S and other datasets
Datasets to which the F and R 30-cycle V3 subsets were compared
Average similaritya ± SD (%)
Pearson's correlation coefficient (F, R)b
Itself (F vs R)
97.1 ± 0.3
93.2 ± 1.5
82.7 ± 0.8
68.5 ± 3.7
64.1 ± 0.5
V3 from other individualsc
64.8 ± 3.5
We identified 206 bacterial genera using 16S-HTS, 108 of which have not been previously found in salivary microbiomes using culture-independent techniques [18, 21–24]. This was also the case with 19 out of 56 genera determined by WGS, and 6 out of 39 genera identified by 16S-WGS approach. The majority of the new salivary genera (116/124) were found at a frequency < 0.1%, and only 8 occurred at a frequency between 0.1 and 0.74% (Additional files 1 and 4). This suggests that the most abundant bacterial genera in the saliva of healthy subjects have probably already been identified. However, the inventory and dynamics of low-abundance-genera, whose identification requires a deeper sample coverage, remain largely unknown.
Using WGS sequencing, which, in contrast to the 16S-HTS method applied in this study, does not specifically target bacteria, we did not detect archaea in the saliva sample. This is not surprising since the only archaeon identified so far in the human oral cavity i.e. Methanobrevibacter oralis, was found in dental plaques associated with pathological processes . BLASTN similarity search against fungal genomes of the NCBI database did not yield any significant hits. The reasons for this may be: (i) an inefficient disruption of fungal cells by the enzymatic procedure used to release DNA from bacteria; (ii) the presence of fungi in saliva under the detection level; (iii) the absence of the relevant fungal genomes in the database. So far, sequences of only six fungal genera (Zygosaccharomyces, Penicillium, Gibberella, Saccharomyces, Aspergillus, Candida), present in the oral cavity of healthy individuals , are available in public databases.
Detection of viral sequences in the WGS dataset using BLASTN
Number of hits a
Human herpesvirus 7
Porcine endogenous retrovirus E
Paramecium bursaria chlorella virus-1 FR483
Enterobacteria phage lambda
Enterobacteria phage phiX174
Streptococcus phage SM1
Metagenomics has the potential to serve as a viral and bacterial infection control strategy in clinical practice because it can discover known as well as new pathogens, and might soon replace many existing typing methods in diagnostics.
HTS of cDNA has already been successfully applied to the detection of new viral pathogens in human serum and liver as well as in the reconstruction of viral genomes [27–29]. Similarly, WGS of a patient's feces samples detected the bacterial pathogen Campylobacter jejuni during but not after an acute diarrheal episode .
At least six double-stranded DNA human herpes viruses (HHV) i.e. Herpes simplex virus 1, Epstein-Barr virus, cytomegalovirus and human herpesviruses 6, 7 and 8 have been detected in saliva using sensitive PCR assays . These viruses are shed in saliva asymptomatically which could facilitate their transmission. Most human adults are infected with HHVs but the prevalence of some HHV is significantly higher in HIV-seropositive persons. In subjects with recurrent oral Herpes simplex virus 1 infections, two other HHVs, HHV-6 and HHV-7 were simultaneously present with a frequency of over 93% . HHvs possibly contribute to periodontitis which, in turn, facilitates virus shed into saliva . Recently, using a metagenomic approach Willner et al.  identified Epstein-Barr virus in a pool of oropharyngeal swabs from 19 individuals.
In our study WGS sequencing applied on the salivary metagenome allowed identification of sequences showing the best similarity to the human herpesvirus 7 as well as to the putative periodontopathic bacteria Porphyromonas gingivalis, Treponema denticola and Aggregatibacter actinomycetemcomitans .
The exact role of bacteria and viruses in periodontitis and other oral diseases is not elucidated. It has been hypothesized that bacteria and viruses cooperate to provoke the disease . The detection of periodontopathic agents is important because periodontitis has been associated with other health problems such as cardiovascular diseases, premature delivery, rheumatoid arthritis and cancer .
Although a large fraction of DNA extracted from saliva corresponds to human DNA, we estimate that, at a coverage consisting of a hundred million sequences, which is the current capacity per channel on the Illumina platform, hundreds of thousands of bacterial sequences and thousands of viral and phage sequences may be identified. Therefore, detection of viruses by metagenomic sequencing is possible even without including filtration and concentration steps, although these procedures are effective in enriching the metagenomic samples for viral DNA . In addition, tens of thousands of 16S rDNA sequences, free of amplification anomalies, may be extracted from huge WGS datasets and used to assess taxonomic composition of bacterial communities.
Analysis of the salivary microbiome is not only of interest from a fundamental perspective, but may have implications in diagnostics e.g. in detection of viruses and microorganisms without including specific tests for each pathogen.
In our study, WGS sequencing compared to 16S-HTS generated a higher fraction of taxonomically unassigned non-human sequences because of the lack of homologs in sequence databases. Using relatively stringent BLASTN parameters about 19% and 35% of sequence reads remained taxonomically unassigned in the forward- and reverse-run WGS subsets, respectively. Nevertheless, the advantage of the WGS approach is that it allows assessment of not only bacterial but also viral (human viruses and phages) and possibly fungal and archaeal communities which undoubtedly play an important role in oral health or disease. In addition, an in-depth sequencing of a salivary metagenome may provide insights into gene functions and allow for reconstruction of the functional potential of a microbial population . Functional assignments of sequences may be made for instance using CAMERA, CARMA3  or MG-RAST , as it was recently shown for the supragingival dental plaque microbome . The obtained sequences may be assigned to known functions and classified to major categories including, among others, virulence and resistance to antibiotics.
List of abbreviations
human herpes virus
high throughput sequencing
operational taxonomic unit
whole genome shotgun.
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