Introduction

The study of the human microbiome focuses predominantly on bacterial flora (Bik et al., 2006; Gao et al., 2007; Costello et al., 2009); however, there are numerous reports of viral communities inhabiting different body sites (Bachrach et al., 2003; Breitbart et al., 2003; Bik et al., 2006; Gao et al., 2007; Breitbart et al., 2008; Costello et al., 2009; Reyes et al., 2010). Although there are viruses of Eukarya inhabiting these sites (Victoria et al., 2009; Foulongne et al., 2012; Lysholm et al., 2012), the majority of the viruses that have been identified in these habitats are bacteriophage (Breitbart et al., 2008; Willner et al., 2009). As has been demonstrated in other environments (Breitbart et al., 2002; Angly et al., 2006), viral communities in the human body are diverse and generally composed of numerous different genotypes. These communities have also been hypothesized to serve as reservoirs of gene function for human microbial communities (Canchaya et al., 2003; Rohwer and Thurber, 2009; Pride et al., 2012a) and may serve particular importance in the mobilization of antibiotic resistance (Colomer-Lluch et al., 2011a, 2011b). Although the constituents of human viral communities may vary substantially within individuals over time, much of the gene function present in viruses of the human oral cavity remains conserved (Pride et al., 2012a).

Similar to viral communities, the relative abundance of bacterial species is variable; however, their presence in the oral cavity of different human subjects is generally conserved (Pride et al., 2012a). Given this relative conservation, bacteriophage in the environment might find suitable bacterial hosts in a variety of human subjects. There is some evidence that suggests that the environment has a role in the shaping of bacterial and viral communities in humans (Willner et al., 2009). We had previously performed a study of a small number of human subjects and found two subjects from the same household (SHH) who shared many homologous sequences between their viral communities (Pride et al., 2012a). Each of these subjects shared similar bacterial biota but their relative abundances were dissimilar, suggesting that shared environment and not the relative abundances of bacteria in the oral cavity may be a significant determinant of viral community constituents in humans.

There is much interest in understanding how our microbiota are established and deciphering how exposure to microbes might ultimately affect community ecology. For human viral communities, little is known about to which viruses human subjects are exposed. CRISPRs (clustered regularly interspaced short palindromic repeats) are part of the CRISPR/Cas system in bacteria and archaea and are considered to be part of an adaptive immune response against invading viruses (Barrangou et al., 2007). They function by acquiring short sequences from invading viruses and utilizing these sequences to resist subsequent exposures to those viruses through nucleic acid interference (Bhaya et al., 2011; Brouns et al., 2008; Hale et al., 2009; Marraffini and Sontheimer, 2009; Sorek et al., 2008; Young et al., 2012). Because CRISPR loci acquire and accumulate short viral sequences (Pourcel et al., 2005; Marraffini and Sontheimer, 2009), they can be used to track viral exposures (Vergnaud et al., 2007; Andersson and Banfield, 2008; Zhang et al., 2010; Pride et al., 2012b; Rho et al., 2012). In this study, we examined the viromes and CRISPRs from 21 human subjects, 11 independent and 10 from among 4 separate households, in an attempt to gain a better understanding of the role that shared environment has in viral community membership and viral exposures in the human oral cavity.

Experimental Procedures

Enrollment of human subjects

Recruitment of each subject and enrollment in the current study was approved by the University of California, San Diego, CA, USA and the Western University Administrative Panels on Human Subjects in Medical Research. Each subject received a baseline periodontal examination before or at the time of saliva collection, which included measurements of probing depths, clinical attachment loss, Gingival Index, Plaque Index and gingival irritation (Loe, 1967). A minimum of 3 ml of saliva was collected from each subject and immediately frozen at −20 °C until further processing. All subjects enrolled were free from non-restored carious lesions and were in good overall periodontal health, with a diagnosis no greater than mild gingivitis. Exclusion criteria for the study included antibiotic administration during the 3 months before sample collection and preexisting medical conditions that could result in substantial immunosuppression. During their visit, subjects who enrolled were asked if they had members of their household willing to participate in the study.

Isolation of viruses, virome sequencing and screening for contamination

Saliva from human subjects was filtered sequentially through 0.45- and 0.2-μm filters to remove cellular debris, and the remaining fraction purified on a cesium chloride gradient as previously described (Pride et al., 2012a). Only the fraction at the density of most known viruses (Murphy et al., 1995) was retained. Viruses were then further purified on an Amicon YM-100 column (Millipore, Inc., Bellerica, MA, USA), treated with DNASE I, followed by lysis and DNA purification using Qiagen UltraSens virus kit (Qiagen, Valencia, CA, USA). Resulting DNA was amplified using GenomiPhi V2 MDA amplification (GE Healthcare, Pittsburgh, PA, USA), fragmented to roughly 100–200 bp using a Bioruptor (Diagenode, Denville, NJ, USA), created into libraries using the Ion Plus Fragment Library Kit according to the manufacturer’s instructions and sequenced using 314 chips on an Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY, USA) (Rothberg et al., 2011) producing an average read length of approximately 100 bp for each sample. Because of the sequencing error rate and the possibility for low-complexity reads, each read was trimmed according to modified Phred scores of 0.5 using CLC Genomics Workbench 4.65 (CLC Bio USA, Cambridge, MA, USA), and low-complexity reads (where >25% of the length were due to homopolymer tracts) were removed before further analysis. After trimming and removal of low-complexity reads, any remaining reads with substantial length variation (<50 nucleotides or >200 nucleotides) or reads with ambiguous characters were removed from the analysis. Remaining reads were analyzed using CLC Genomics Workbench 4.65 to construct assemblies based on 98% identity with a minimum of 50% read overlap, consistent with criteria developed to discriminate between highly related viruses (Breitbart et al., 2002). Because the shortest reads were 50 nucleotides, the minimum tolerable overlap was 25 nucleotides, and the average overlap was no <50 nucleotides depending on the characteristics of each virome. Contigs <200 bp were removed from further study, and the remaining contigs were assigned to their corresponding phylum based on their BLASTX best hits from the National Center for Biotechnology Information (NCBI) non-redundant database with an E-score cutoff value of 10−5. Specific viral homologues were determined by parsing BLASTX results for known viral genes, including replication, structural, transposition, restriction/modification and hypothetical, and other genes previously found in viruses for which the E-score was at least 10−5. Analysis of shared homologues present in each virome was performed by creating custom BLAST databases for each virome, comparing each database with all the other viromes using BLASTN analysis (E-score <10−5), and normalization to the size of the smaller virome. Principal coordinates analysis was performed on homologous virome reads using binary Sorensen distances using QIIME (Caporaso et al., 2010). Read mapping of viromes to a combined database of viruses (http://www.phantome.org; ftp://ftp.ncbi.nih.gov/genomes/Viruses/) was performed using CLC Genomics Workbench 4.65 and were mapped using 98% identity over a minimum of 50% of the read length. The read mapping results were reported as the proportion of reads mapping to any virus compared with the total number of virome reads. Virome sequences are available for download in the MG-RAST database (metagenomics.anl.gov/) under the project ‘Household Study,’ under individual accession numbers 451142.3, 451143.3, 451144.3, 451145.3, 451146.3, 451147.3, 451148.3, 451149.3, 451150.3, 451151.3, 451152.3, 451153.3, 451154.3, 451155.3, 451156.3, 451157.3, 451158.3, 451159.3, 451160.3, 451161.3 and 451162.3.

CRISPR sequencing and analysis

From each subject, genomic DNA was prepared from saliva using the Qiagen QIAamp DNA MINI Kit (Qiagen), with the addition of a bead beating step using Lysing Matrix B (MPBio, Solon, OH, USA) before DNA extraction. CRISPR sequences were amplified based on primers designed from the palindromic repeat sequences of various CRISPRs (Supplementary Table S2). Primer pairs for Gemella haemolysans group I (GHI), Veillonella species group I (VSI), Leptotrichia buccalis group I (LBI), Streptococcus group II (SGII) and SGI CRISPR spacers contain 10-nucleotide barcode sequences (represented by the ‘X’) and were used to amplify CRISPR sequences from each subject (Supplementary Tables S2 and 10). Reaction conditions included 44 μl Platinum High-Fidelity PCR Mastermix (Invitrogen, Carlsbad, CA, USA), 1 μl of each of the forward and reverse primer (10 mmol each) and 4 μl DNA template. The following were used as cycling parameters: 2 min initial denaturation at 94 °C, followed by 30 cycles of denaturation (15 s at 95 °C), annealing (15 s), and extension (2 min at 72 °C), followed by a final extension (10 min at 72 °C). CRISPR amplicons were gel extracted using the Qiagen MinElute Kit (Qiagen). Molar equivalents were determined for each product using an Agilent Bioanalyzer HS DNA Kit (Agilent, Santa Clara, CA, USA), and each were pooled into equimolar equivalents. Resulting pools were sequenced on 314 chips using an Ion Torrent Personal Genome Machine according to the manufacturer’s instructions (Life Technologies) (Rothberg et al., 2011). Barcoded sequences were then binned according to 100% matching barcodes. Each read was trimmed according to modified Phred scores of 0.5, and low-complexity reads and reads with ambiguous characters were removed before further analysis. Only those reads that had 100% matching sequences to both the 5′ and the 3′ end of the CRISPR repeat motifs were used for further evaluation. Spacers were defined as any nucleotide sequences (length ⩾20) in between repeat motifs. To group spacers according to their trinucleotide content, we first compiled the trinucleotide content for all spacers and added them to a database. For each sequence, the difference in trinucleotide content was compared between all possible pairs of sequences regardless of overall spacer length, as length differences between identical spacers over the length of the shorter spacer would only account for small differences in total trinucleotide content. The sum of the differences for all sequence pairs was then determined for all sequences, and then spacers were binned together if their differences were less than the s.d. from the mean overall difference. Charts were created that included the total number of spacers with a specified number of trinucleotide differences. To validate the technique, random data sets of spacers were created with known numbers of mutations (including indels and polymorphisms). For each specified number of indels or polymorphisms, 1000 data sets with 5000 spacers each were created. For each data set, we took a single spacer and created 10, 100 or 500 different mutations in that spacer. We either created a single mutation in each spacer, or we created 2, 3 or 4 mutations. We then introduced these spacers back into the data set and binned the spacers according to their trinucleotide content differences. When a single mutation was introduced, regardless of whether the source of that mutation was an indel or a polymorphism, the mutated spacers were always binned together into a single spacer group.

For each subject evaluated, a database of spacer groups was generated, and databases were compared to determine shared spacer groups and to create heatmaps using Java Treeview (Saldanha, 2004). Beta diversity was determined using binary Sorensen distances and was used as input for principal coordinates analysis using Qiime (Caporaso et al., 2010). Spacers from each subject were subjected to BLASTN analysis based on the NCBI non-redundant database. Hits were considered significant based on bit scores ⩾45, which roughly correlated to 2 nucleotide differences over the length of a 30 nucleotide spacer. CRISPR spacers for each subject were used to search a database of the virome reads for matches from all viromes combined, and the number of spacer matches per virome read was used to create heatmaps. The heatmaps were normalized by the total number of spacer matches per virome read and were generated using Java Treeview (Saldanha, 2004). Rarefaction analysis was performed based on spacer group richness estimates of 10 000 iterations using EcoSim (Lee et al., 2005). Good’s coverage was determined as the estimation of the number of singletons in the population (n), compared with the total number of sequences (N), using the equation (1−(n/N)) × 100 (Good, 1953).

CRISPR loci from Streptococcus oralis were amplified using primers 5′-CGCCAAAAATCCGTATGAAA-3′ and 5′-TCGTAAAGTGTGGGCTCTCC-3′ and Streptococcus thermophilus CRISPR loci were amplified using primers 5′-CTGAGATTAATAGTGCGATTACG-3′ and 5′-GCTGGATATTCGTATAACATGTC-3′. CRISPR amplicons were gel extracted using the Qiagen MinElute Kit (Qiagen) and sequenced in both the directions using conventional Sanger sequencing.

Statistics

To assess whether virome reads or spacer groups had significant overlap between the set of individuals within the SHH, we performed a permutation test. We simulated the distribution of the fraction of overlapping reads within a group of individuals from mutually exclusive households that were randomly chosen across all the different households (DHHs) for numbers of individuals equal to the sizes of household nos. 1, 2, 3 and 4, respectively. For each set, we computed the summed fraction of randomly chosen spacer groups or virome reads and from these computed an empirical null distribution of statistics. The fraction computed resulted from 10 000 iterations for both the spacer groups and virome reads. For the CRISPR spacer groups, 1000 spacer groups were sampled in each iteration, and 10 000 reads were sampled in each iteration for the virome reads. The s.d. was computed from the percentage of homologous virome reads or spacer groups over the 10 000 iterations. For each household, an empirical null distribution of statistics was determined from an equal number of subjects from DHHs. The observed statistic per household was referred to this distribution, and the P-value was computed as the fraction of times the simulated statistic for the SHH exceeded the simulated statistic for the DHHs.

Results

Isolation and sequencing of salivary viruses

We recruited and sampled saliva from 21 human subjects in good overall periodontal health (Table 1). Eleven of the subjects were from DHHs, and 10 of the subjects were from 4 separate households (3 subjects in household no.1, 3 subjects in household no. 2, 2 subjects in household no.3 and 2 subjects in household no.4). All of the subjects residing in the SHHs were unrelated; however, 3 of the subjects (DHH5, DHH11 and SHH4-1) were related but had resided in DHHs for at least 9 years (Table 1). DNA viruses were isolated from the saliva of each subject through sequential filtration followed by cesium chloride density gradient centrifugation (Pride et al., 2012a). We then sequenced 10 685 110 virome reads (average of 508 815 reads per subject) using semiconductor sequencing (Rothberg et al., 2011). To ensure that the viral communities were properly separated from potential contaminating cellular elements, we screened each virome against 16S ribosomal RNA and reference human databases. We found the vast majority of the viromes were free of substantial numbers of homologues to human DNA or bacterial 16S ribosomal RNA (Table 2). The largest number of human homologues from a virome (DHH2) measured at 0.17% (592 of 346 558) of the sequenced reads. These data indicate that the viromes studied have a relatively insignificant amount of measurable contaminating DNA from cellular elements.

Table 1 Study subjects
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Table 2 Virome reads and contigs
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Virome composition

We assembled the reads from each virome into larger contigs (Table 2) to help identify the most abundant viruses present in the saliva of each subject. Each contig then was subjected to BLASTX analysis using the NCBI non-redundant database to identify homologues present in the viromes. Similar to other studies (Breitbart et al., 2008; Pride et al., 2012a), the vast majority of the homologues found were to bacteriophage, while only a few homologues to viruses of Eukarya (herpesviruses and circoviruses) were found. By using the host taxonomy of each homologue as the putative taxonomy for each contig, we determined the host taxonomy of our viral communities at the Phlyum level. We found that most of the subjects had a predominance of viruses from the phyla Proteobacteria or Firmicutes (Figure 1), which includes the genera Streptococcus, Gemella and Veillonella. We also found many Fusobacteria, which includes the genus Leptotrichia. With the exception of household no.3, there were similar proportions of phyla for the subjects within the SHHs; however, similar patterns were also observed in subjects from DHHs.

Figure 1
figure 1

Putative host biological assignments for viral contigs from human saliva from all the subjects. The percentage of contigs assigned to each biological group based on BLASTX best-hits is shown for each subject.

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We mapped the virome reads from each subject to a database of known bacteriophage (www.phantome.org) to determine the proportion of virome reads from each subject that mapped to known bacteriophage. In some subjects, a substantial proportion of the reads mapped to a single bacteriophage in the database, suggesting that these bacteriophage are highly abundant (Supplementary Table S1). This is contrary to results we previously reported for human salivary viromes in which the oral viral populations were predicted to be evenly distributed in all the subjects studied (Pride et al., 2012a). For subject DHH5, 13% (75 581 reads) of the virome reads mapped to Streptococcus phage 5093, while 10% (53 880) of the reads from subject DHH11 map to the same phage. These subjects were related (Table 1) but had not resided in the SHH for the past 15 years. Subject SHH4-1, the daughter of subject DHH5, had a much lower abundance of virome reads (<1%) that mapped to Streptococcus phage 5093. Numerous other bacteriophage were identified by mapping virome reads against the database of bacteriophage, including Actinomyces phage AV-1 (4% in DHH3) and Enterobacteria phage DE3 (10% in DHH8) (Table 2). Nearly the entire bacteriophage genomes of Streptococcus phage 5093, Actinomyces Phage AV-1 and Enterobacteriaphage DE3 were present in these viral communities (Figure 2). We found other bacteriophage present in these communities whose full genome likely is present, including Haemophilus phage HP-1, despite representing <1% of the virome reads in subject SHH3-2 (Figure 2d).

Figure 2
figure 2

Read mappings of viromes to select viruses. Panel (a) demonstrates the mapping of reads from subject DHH5 to Streptococcus phage 5093 (75 581 of the 572 571 reads). Panel (b) demonstrates the mapping of reads from subject DHH3 to Actinomyces phage AV-1 (21 076 of the 473 775 reads). Panel (c) demonstrates the mapping of reads from subject DHH8 to Enterobacteria phage DE3 (18 776 of the 188 093 reads). Panel (d) demonstrates the mapping of reads from subject SHH3-2 to Haemophilus phage HP-1 (1406 of the 376 492 reads). The y axis demonstrates the total number of reads mapping to an individual portion of each virus.

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Shared viral homologues within households

To determine whether there was an association between living environment and the composition of the oral viral community, we compared the virome reads among all the subjects studied. Using BLASTN analysis to find homologues between all subjects, we found a higher percentage of shared homologues between some household members (Figure 3a and Supplementary Figure S1). For household no.1, SHH1-1 and SHH1-2 shared the most homologous sequences, while SHH1-3 shared the most homologous sequences with DHH9. For household no.2, SHH2-2 shared the most homologous sequences with SHH2-3, and SHH2-1 shared the most homologous sequences with DHH11. For household no.3, SHH3-1 and SHH3-2 shared the most homologous sequences. Subjects SHH4-1 and SHH4-2 shared the most homologous sequences with subjects outside of their household. We next tested whether the fraction of homologous virome reads within each household would be greater than that between DHHs by randomly sampling the virome reads >10 000 iterations. We found that there were significant relationships found in households nos.1, 3, and 4 (P<0.0001 for each) (Table 3). Thus, three of the four households studied harbor subjects with a higher fraction of homologous sequences among their viromes than would be expected if they resided in DHHs, suggesting that shared living environment has a role in shaping salivary viral ecology. We also used principal coordinates analysis of the viral homologues to determine whether there was an association between shared living environment and virome sequences (Figure 3b). The analysis supported that there was some association among the virome sequences within households nos.1 and 3. Subjects DHH5 and SHH4-1 (mother and daughter) did not have a significant association; however, DHH5 and DHH11 (siblings) shared many virome sequences (Figure 3a).

Figure 3
figure 3

Homologous reads matrix (a) and principal coordinates analysis (b) of viromes from all the subjects. The matrix demonstrates the percentage of homologous reads shared between all subjects. The top half of the matrix represents comparisons between subjects residing in DHHs, the bottom half of the matrix represents comparisons between subjects from the four separate shared households and the middle portion of the matrix outlined in purple represents comparisons between DHHs and the four separate shared households. Comparisons between subjects who reside in the SHH are outlined in blue. The principal coordinates analysis is based on beta diversity metrics of all shared virome reads from all the subjects. Household no.1 is represented in green, household no.2 is represented in cyan, household no. 3 is represented in red and household no.4 is represented in yellow.

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Table 3 Viral homologues between households
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Grouping CRISPR spacer

CRISPR loci expand at the 5′ end as new virus-specific spacers are added after each viral encounter. By analyzing the CRISPR loci, we could trace past viral exposures. Because our analysis of salivary viruses was based on a single time point, we chose to explore CRISPRs in each subject to track past viral exposures. Rather than examine individual CRISPR loci, we chose a metagenomic approach. The benefits of a metagenomic approach were that we could examine multiple loci simultaneously, explore loci from bacteria not known to be present or to harbor CRISPR loci and examine CRISPR loci from abundant and relatively rare microbes. Although this approach produced substantial data from many likely streptococcal species, the major limitation was that CRISPR spacers could not be ordered into individual loci or attributed to individual loci or species.

We developed a technique to bin CRISPR spacers based on trinucleotide content so that spacers targeting similar viruses would be grouped together. We chose such a technique because depending on the characteristics of the data set, similar spacers that harbor multiple polymorphisms could be binned together without setting an arbitrary similarity cutoff. When binning spacers based on trinucleotide content, those spacers that differ by a single polymorphism or indel (insertion or deletion) will have similar trinucleotide content differences. We validated the technique by generating sets of 5000 random spacer sequences. Then using these spacer data sets, we took a single spacer and created random mutations along its length. We introduced 10, 100 or 500 mutated spacers to the random spacer data set, and then tested whether the technique could group these mutated spacers into a single spacer group. When a single polymorphism or indel was introduced, the mutated spacers were always binned together in a single spacer group regardless of the number of mutated spacers that were introduced (Table 4). Similar results were also produced when we introduced two polymorphisms or indels. The technique was less robust when three or four polymorphisms or indels were introduced, with the mutated spacers forming more than a single spacer group. Although the spacers formed more groups when more than two polymorphisms were introduced, these groups were always populated only by other mutated spacers.

Table 4 Grouping of mutated spacers
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CRISPR spacer metagenomes

We sequenced CRISPR loci using primers based on five separate repeat motifs found in oral bacteria Gemella haemolysans (GHI), Veillonella sp. (VSI), Leptotrichia buccalis, Streptococcus gordonii (SGII) and Streptococcus mutans (SGI) (Supplementary Table S2). Because the streptococcal repeat motifs have been shown to be present in many different streptococcal species (Pride et al., 2011), we presumed the same principle might apply to GHI, VSI and LBI CRISPR repeat motifs. We identified 2 065 246 CRISPR spacers from the 21 subjects studied. For GHI, we identified 360 429 CRISPR spacers in 1706 spacer groups based on trinucleotide content (Supplementary Table S3). The distribution of the trinucleotide content differences between the GHI spacers demonstrated that the majority of the spacers were either identical or highly similar, with only 0.003% of the GHI spacers identified as having polymorphisms or indels that necessitated grouping according to trinucleotide content (Supplementary Figure S2A). Similar results were produced for VSI (0.003% had polymorphisms), LBI (0.009% had polymorphisms), SGII (0.002% had polymorphisms) and SGI spacers (0.001% had polymorphisms) (Supplementary Tables S4–7 and Supplementary Figures S2B–E). These data indicate that there were relatively few polymorphisms in each population of CRISPR spacers, potentially the result of sequencing error.

We subjected the CRISPR spacer population in each subject to rarefaction analysis to estimate how thoroughly each subject had been sampled for each CRISPR type. The shapes of many of the curves indicated that further sampling would not have identified many more CRISPR spacer groups (Figure 4). For example, LBI spacers for each subject reached asymptote (Figure 4c), with the exception of one subject. LBI CRISPRs had relatively low spacer richness compared with other CRISPR types, while SGI CRISPRs (Figure 4e) generally had the highest spacer richness. Shared living environment generally did not predict CRISPR spacer richness. Although a few curves represented continued expanding diversity (Figure 4), the Good’s Coverage estimates were relatively high (range from 90 to 100) and indicated that the majority of unique spacers in each subject and CRISPR spacer type had been adequately sampled (Supplementary Tables S3–7).

Figure 4
figure 4

Rarefaction analysis of CRISPR spacers in the saliva of all the subjects. Rarefaction curves were created using 10 000 random iterations based on spacer group richness. (a) GHI CRISPR spacers; (b) VSI CRISPR spacers; (c) LBI CRISPR spacers; (d) SGII CRISPR spacers; (e) SGI CRISP spacers.

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Shared CRISPR spacers

To improve our understanding of the conservation of CRISPR spacer groups in all the subjects, we generated heatmaps (Figure 5). Each subject had a large proportion of CRISPR spacer groups that were subject specific for all CRISPR spacer types, while a minority of the spacers was shared between different subjects. For most subjects, no specific trend of shared spacer groups was seen; however, there were some subjects who shared a proportion of spacer groups depending on the CRISPR type. For example, subjects DHH4 and DHH5 had similar trends in GHI spacer group conservation (Figure 5a) and subjects DHH1, DHH2, DHH8 and DHH11 shared a substantial portion of SGII spacer groups (Figure 5d).

Figure 5
figure 5

Heatmaps of the CRISPR spacer groups in all the subjects. Each row represents a unique spacer group and the columns represent subjects from DHHs or subjects from the four separate shared (same) households. The intensity scale bar is located to the right. (a) GHI CRISPR spacers; (b) VSI CRISPR spacers; (c) LBI CRISPR spacers; (d) SGII CRISPR spacers and (e) SGI CRISPR spacers.

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We determined the proportion of CRISPR spacer groups shared among all the subjects and found a trend similar to that found for viromes. In household no.2, subject SHH2-2 shares the most CRISPR spacer groups with subject SHH2-3 for GHI, VSI, LBI, SGII and SGI type CRISPR spacers (Figures 6a–e). Subjects SHH3-1 and SHH3-2 in household no.3 also share the most spacer groups for all types of CRISPR spacers (Figure 6) and viromes (Figure 3). As was also seen for viromes, there were generally fewer spacer groups shared between the subjects in household no.4 than for other households. We also measured the fraction of shared spacer groups that were present in each household and compared with that from an equal number of randomly selected DHHs. In each instance, the fraction of shared spacer groups within a household was greater than would be expected when comparing individuals from DHHs (Table 5), and most results were highly significant (P<0.0001). Only VSI and LBI CRISPR types in household no.4 did not show a significant association between household and CRISPR spacer groups. Of the three related subjects (DHH5, DHH11 and SHH4-1), there were few spacer groups shared. We also sequenced several individual CRISPR loci from S. oralis and S. thermophilus and found a high diversity of CRISPR loci among the different subjects (Supplementary Figure S3). Interestingly, we found a few shared spacers among the different subjects, but the CRISPR spacer order was not conserved (Supplementary Figure S3B). These data indicate that the CRISPR spacers in these loci may have been acquired independently and suggest that some of the shared CRISPR spacers among our subject population are the result of independent CRISPR locus evolution rather than vertical or horizontal transmission.

Figure 6
figure 6

CRISPR spacer group matrix from all the subjects. The matrix demonstrates the percentage of shared CRISPR spacer groups between all the subjects. The top half of the matrix represents comparisons between subjects residing in DHHs, the bottom half of the matrix represents comparisons between subjects from the four separate shared households and the middle portion of the matrix outlined in purple represents comparisons between DHHs and the four separate shared households. Comparisons between subjects who reside in the SHH are outlined in blue. (a) GHI CRISPR spacers; (b) VSI CRISPR spacers; (c) LBI CRISPR spacers; (d) SGII CRISPR spacers and (e) SGI CRISPR spacers.

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Table 5 Shared CRISPR spacers within households
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We examined each of the CRISPR types by principal coordinates analysis to determine whether close relationships existed in CRISPR spacer repertoires from subjects in shared living environments (Supplementary Figure S4). For household no.3, a close relationship in their CRISPR spacers is demonstrated for all CRISPR spacer types, except LSI (Supplementary Figure S4C). Subjects SHH2-2 and SHH2-3 in household no.2 also share close relationships in their CRISPR spacer content. There was little relationship discovered between the subjects in household no.4, with the exception of SGII CRISPR spacers (Supplementary Figure S4D). In general, there were closer relationships within households for the streptococcal CRISPRs (Supplementary Figures S4D–E) than for the other CRISPR types (Supplementary Figures S4A–C). These data suggest that shared living environment may influence viral exposures in human saliva; however, these results could also be explained by shared ancestry of CRISPR loci.

CRISPR spacers match virome reads

We tested whether the CRISPR spacers had any homologues in the NCBI non-redundant database to better define the group of viruses to which our subjects may have had previous exposures. Only a single homologue was found for GHI spacers among the 1706 spacer groups from all the subjects (Supplementary Table S8). There were few homologues in the database to the 1990 VSI spacer groups, with all of them found in the Veillonella parvula DSM 2008 genome. We found homologues to LBI spacers matching sequences outside of the CRISPR loci in the genomes of L. buccalis and Sebaldella termitidis, both belonging to the phylum Fusobacteria. There were abundant homologues found to SGII and SGI spacers (Supplementary Tables S8 and 9), likely as a result of the large database of previously sequenced streptococci and streptococcal viruses.

Although there were relatively few CRISPR spacers that had homologues in the NCBI non-redundant database, we tested whether there were homologues present in the virome reads from all the subjects. In nearly every circumstance, there were more virome reads than NR database homologues matching CRISPR spacers (Supplementary Table S8), indicating that oral spacer repertoires were specifically adapted to oral viromes. Similar to a previous study (Pride et al., 2012b), there was no specific pattern of CRISPR spacers repertoires matching virome reads within that same subject, but rather many subjects have CRISPR spacers that match viruses from other subjects (Supplementary Figure S5). Similar results for SGI and SGII spacers were found for virome contigs to those that were found for virome reads (Supplementary Figure S6). Indeed, the vast majority of the virome reads that matched CRISPR spacers were found in the viromes of subjects DHH4, DHH5, DHH6, DHH11 and SHH1-2. Because the majority of the matches were to streptococcal viruses (Supplementary Table S9), these data suggest that those subjects were highly enriched for streptococcal viruses.

There was a much lower percentage of each type of CRISPR spacer that had homologues in the NR database (Figure 7a) than in the virome reads (Figure 7b). We found a small percentage of CRISPR spacers that matched virome reads for GHI, VSI and LBI type CRISPRs, indicating that these viromes likely have been targeted by these CRISPR types (Figures 7a–c). Importantly, Veillonella and Leptotrichia are genera of bacteria that usually live in the anaerobic environment of the subgingival crevice. Our data indicate that viruses of anaerobic bacteria were also present in the salivary environment (Figures 1 and 7). There were many more CRISPR spacers that matched virome reads for the streptococcal CRISPR types SGI and SGII. Most interestingly, 75% of the SGII CRISPR spacer groups in subject DHH1 were homologous to virome reads from the subjects in this study. Similar results were found for SGII and SGI CRISPR types in other subjects, including DHH2, DHH11 and SHH4-1. We believe the high percentage of CRISPR spacers matching virome reads represents specific adaptation of bacteria in the human oral cavity to viruses commonly encountered in the salivary environment.

Figure 7
figure 7

Percentage of the CRISPR spacer groups with homologues in the NCBI non-redundant database (a) and in the viromes of all the subjects (b). The y axis represents the percentage of the CRISPR spacer groups, and the x axis represents the different CRISPR spacer types. The boxes on the x axis represent the DHHs, where the 11 subjects from DHHs are represented by the large box (from DHH1 to DHH11), and the subjects in the four separate households are represented by the smaller boxes (from household no.1 subjects to household no.4 subjects, left to right).

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Discussion

Our data suggest that shared living environments influence the viral microbiota of the human oral cavity. Although not every subject in the SHH demonstrated this trend, the viruses in households nos.1, 3 and 4 shared a significantly greater than expected fraction of homologous reads (Table 3). Although it seems plausible that virome constituents and viral exposures would directly reflect the bacterial communities present, we chose not to evaluate the bacterial communities for the following reasons: (1) we previously have demonstrated that inverse relationships exist between certain viruses and their host bacteria in the human oral cavity (Pride et al., 2012a, 2) much of the variation in CRISPR communities likely exists at the strain level and thus cannot be evaluated through 16S rRNA sequencing, and (3) there have already been relatively thorough evaluations of the bacterial communities present in saliva (Bachrach et al., 2003; Lazarevic et al., 2009; Nasidze et al., 2009; Bik et al., 2010; Pride et al., 2011a, Pride et al., 2011;). Thus, we do not believe that sequencing of 16S rRNA would clarify the effects of bacteria on the viral community constituents within households.

Contamination of viromes with nucleic acids from human and bacterial cells could potentially influence the results in any study of human viral communities. We have developed highly stringent criteria to ensure that the viromes studied are relatively free of cellular contamination to prevent apparent similarity between viromes because of shared contaminating nucleic acids. Generally, most, if not all, of the cellular material was removed through sequential filtering through 0.2-μm filters, though free contaminating nucleic acids could remain. Centrifugation of the remaining materials on a cesium chloride density gradient separated the viral fraction from free nucleic acids and cellular materials, and the addition of a DNase digestion step was added to remove any naked nucleic acids before further processing. We then screened all sequenced virome reads for human DNA by BLASTN analysis against the NCBI human genome assemblies. None of the viromes we sequenced had a significant percentage of reads that were homologous to the human genome (Table 2), and those virome reads were removed before further analysis to ensure that these homologues could not affect the analysis of homologous sequences between viromes. Because the putative phyla distribution in these viromes is similar to that found for the bacterial communities in saliva (Pride et al., 2012a, Pride et al., 2011), we also screened for evidence of contaminating bacterial DNA by BLASTN analysis against a composite 16S rRNA database. Of the 21 viromes we sequenced, only DHH2 had a significant number of 16S rRNA homologues (Table 2). All five 16S rRNA homologues were to staphylococcal genomes, which are generally not normal flora of the human oral cavity. No significant numbers of DHH2 virome reads were homologous to staphylococcal genomes, suggesting that these genomes were not contaminated with staphylococcal nucleic acids. In total, our analysis indicates that these viromes are relatively free of contaminating cellular nucleic acids. We also replicated the virome read data for household no.2 to demonstrate that the data were reproducible. However, because of the lack of homologous sequences among the subjects within household no.2 (Table 3), the replicates were not informative. The replicate data were combined with the original virome reads for the subjects in household no.2, and each treated as a single virome throughout the study.

The extraordinary diversity of viruses in human ecosystems can often serve as a limiting factor in assembly and analysis of human viral communities. We previously showed that viral communities in the human oral cavity are inhabited by many different viral genotypes with a relatively even distribution (Pride et al., 2012a). The results of those data were highly dependent on the ability of assembly tools to create contig spectra that were representative of the viral population. We used several different assemblers on our virome reads; however, most produced similar contig spectra consistent with an evenly distributed population using PHACCS (Angly et al., 2005). We noticed in our analysis of the virome communities from the subjects in this study that in many of the viromes a substantial proportion of the reads mapped to a single virus (Figure 2 and Supplementary Table S1). Virome read distributions from subjects DHH5, DHH8 and DHH11 suggested that Streptococcus phage 5093, Actinomyces phage AV-1 and Enterobacteria phage DE3 or highly similar viruses were abundant in these subjects and that the populations were not evenly distributed. There are other tools such as MaxiΦ (Angly et al., 2006) available for analysis of viromes, but they also are based on contig spectra, which we do not believe provide an accurate description of the viromes produced in this study.

Because we did not evaluate CRISPR spacers derived from Enterobacteria or Actinomyces, we could not discern a bacterial response to their viruses being present in high abundance in this study (Figure 2 and Supplementary Table S1). Surprisingly, we did not find CRISPR spacers matching Streptococcus phage 5093 in the subjects harboring this virus, but we did find spacers matching this virus in other subjects (data not shown). There was a substantial repertoire of CRISPR spacers found for SGI and SGII CRISPR spacers that matched many different species of Streptococcus (Supplementary Table S9). Interestingly, the spacers generally were not identical to those of the streptococcal CRISPR loci in these genomes but rather were identical to putative lysogenic phage in the streptococcal genomes, which limited our ability to use existing CRISPR loci as a template for assembly of the CRISPR spacers sequenced in this study. We found many spacers for SGI and SGII type spacers that matched bacteria other than streptococci, which we had not found in our previous analysis of these types of CRISPR spacers. We believe that the discovery of these spacers is the result of the greater sequence depth provided in this study and suggests that these CRISPR spacer types may be present in other organisms beyond the Genus Streptococcus.

Our data supports that shared living environment affects the composition of the oral viral community (Figure 3b), but is only one of many possible factors that might affect oral viral ecology. Other factors that might affect shared viral ecology include diet, shared bacterial biota or shared ancestry of the study subjects, which might explain the similar CRISPR and virome profiles from siblings DHH5 and DHH11. By the very nature of saliva, its viruses may be transitory and thus not the optimal means of assessing the effects of shared living environments on viral populations. The oral biofilm might represent a more stable population of viruses; however, its relatively low biomass complicates isolation and identification of virome constituents. Based on our findings, the length of time in the shared household may not be a critical factor affecting the viral flora, as subject SHH1-1 has viruses similar to subjects SHH1-2 and SHH1-3 after only a year in the SHH, while subjects SHH2-1 and SHH2-2 share few viral constituents after 3 years in the SHH. We believe that viral exposures are a critical determinant of oral viral ecology, and the CRISPR spacer repertoires from the subjects in all the households strongly suggest that each household member has been exposed to similar viral populations (Table 5). With the exception of VSI and LBI spacers for household no.4, the data strongly support that viral exposures are associated with shared living environment for all households and CRISPR type. Although it is possible that some CRISPR loci are similar merely as a result of shared ancestry of bacterial strains, we believe that this would not explain the full extent of the shared spacers in some subjects. Using data from the CRISPRs web server (Grissa et al., 2007), we estimated that the average streptococcal CRISPR locus has 16 spacers. Thus for household no.3, each subject would need to share 6–7 average-sized SGII CRISPR loci and 10–11 identical SGI CRISPR loci to reproduce the results presented here. Because these CRISPR loci belong to many unidentified species and strains of Streptococcus and the short-read metagenomics approach used to sequence the CRISPR spacers, we were not able to assemble and determine the spacer order or the strain from which each spacer was derived. We identified S. oralis and S. thermophilus CRISPR loci by more conventional techniques and demonstrated that individuals who share some CRISPR spacers often have relatively disparate CRISPR loci (Supplementary Figure S3).

As we continue to explore the human microbiome, we now have a greater understanding of those features that affect viral community ecology and those viruses to which human subjects are exposed. We previously demonstrated that for many host/virus relationships in human saliva there is an inverse relationship, indicating that the relative abundance of bacteria in the oral cavity does not necessarily reflect the relative abundances of certain viruses (Pride et al., 2012a). Although these viruses must be inexorably linked to their host bacteria, as long as a suitable host is present, viruses may be capable of colonizing the oral cavity. Thus, we believe that exposure to viruses is a critical determinant of viral community membership. Our findings that many of the subjects in shared living environments also share similar CRISPR repertoires suggests that they have been exposed to similar viruses; however, additionally this association could be secondary to shared bacterial strains between subjects. Regardless of their method of acquisition, these CRISPR spacers likely endow these human subjects with similar capacities to resist oral viruses. Because there were similar viruses in subjects in some of the shared living environments, developing similar CRISPR repertoires would provide a selective advantage to those CRISPR-bearing microbes in those shared environments. Interestingly, almost all subjects share some streptococcal CRISPR spacers (Figures 6d and e), suggesting that they all have encountered similar streptococcal viruses. We believe the CRISPR spacers that are shared between subjects may represent a practical benefit of CRISPR-mediated resistance, where they provide innate immunity against viruses they have yet to encounter but to which they carry matching spacer sequences.