To our knowledge, this is the first study to compare the core microbiomes of saliva and tonsils in Korean pediatric patients without tonsillitis. By employing 16 s rRNA gene sequencing, we determined the relative abundance of the microbial community in saliva and tonsils, with a view to establishing the association between the oral saliva microbial community and tonsillar microbiome profile in Korean children (Fig. 6). Our data suggest that the microbiome profiles of saliva and tonsils are largely similar (87.5%), suggestive of interactions between their microbial components. To date, most studies on pediatric tonsils have reported the tonsil microbiomes under inflammatory conditions such as tonsillitis. Our study is distinct from these previous studies, in that we examined saliva and tonsil microbiomes in the absence of tonsillitis. Nevertheless, our study could not elucidate the microbiome of perfectly healthy tonsils, because the tonsil microbiota samples used in this study were obtained from patients diagnosed with tonsillar hypertrophy. However, because children with normal tonsils do not need to undergo tonsillectomy, it is almost impossible to obtain perfectly normal tonsil tissues.
Since tonsils have emerged as an good source of adult stem cells in tissue engineering and regenerative medicine with the added advantages of noninvasiveness of tissue collection , relatively high proliferation rate and low allogenicity, our research may facilitate the development of microbiome-based tools to regulate tonsil-derived therapeutic platforms. Further studies on larger groups are warranted to confirm initial findings.
Recent advances in sequencing technology and metagenomics have expanded our knowledge of the composition and association of the oral microbiome with human health and disease. The oral cavity microbiome has generally been examined by collecting oral rinse samples, including saliva, but it remains to be established whether saliva provides an accurate representation of the microbiome of the oral cavity . Given that the human oral cavity is composed of various subsites, including teeth, gingival sulcus, tongue, hard and soft palates, and tonsils, which provide appropriate space for colonization but slightly different environments for microorganisms, the salivary microbiome profile may not be correlated with those of other subsites of the oral cavity.
Although a number of differences in the microbial profiles between recurrent tonsillitis and tonsillar hypertrophy pediatric groups have been determined by Jeong et al.  using culture-based analysis, these experiments were limited in that they only included aerobic bacteria existing in the tonsillar core and many human associated microorganisms are not cultivable under laboratory settings. Accordingly, we aimed to elucidate and compare the core microbiome profiles from paired saliva-tonsil samples using metagenome-wide analysis.
We obtained an average of 12,745 raw read counts, which was markedly lower than the 80,829 raw read count average reported by Jensen et al. . However, Caporaso and co-workers  demonstrated that 2,000 reads are sufficient to resolve relationships among samples observed with the full dataset. Accordingly, we considered our data adequate to determine microbiome correlations between saliva and tonsils. Our dataset identified from 12,745 reads included 1,678 OTUs in saliva and 1,461 OTUs in tonsils, which were reasonable counts for further analysis.
Alpha-diversity indices, including Chao1 and Shannon-Weaver diversity indicies, are generally used to compare the differences between microbial communities [21–23]. Both saliva and tonsils showed similar richness in microbial communities but the saliva microbiome presented higher evenness than tonsil tissue. The microbiome profile of saliva appears more stable relative to that of tonsils. Saliva is constantly secreted from salivary glands into the oral cavity containing a diverse bacterial population while the microbial community in tonsils is mainly colonized in the recessed epithelium of deeply branched crypts [18, 24]. Therefore, saliva continuously secreted from various oral sites contains diverse bacteria but the range in tonsils is lower since the microbial populations stagnate in the crypts and remain limited.
Lazarevic et al.  identified the main phyla in saliva as Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, Fusobacteria, Spirochaetes, and Saccharibacteria. Pyrosequencing analysis of saliva microbes in healthy Chinese children and adults by Ling et al.  disclosed Streptococcus, Prevotella, Neisseria, Haemophilus, Porphyromonas, Gemella, Rothia, Granulicatella, Fusobacterium, Actinomyces, Veillonella, and Aggregatibacter as major components of the healthy saliva microbiome. In our study, Streptococcus was the most abundant component, followed by Haemophilus, Veillonella, Neisseria, Fusobacterium, Prevotella 7, and similar to the profile determined by Ling and co-workers .
In a study on bacterial distribution in tonsils of Korean children, Jeong et al.  isolated 966 microbes from the tonsil cores of 824 recurrent tonsillitis and tonsillar hypertrophy patients using the cultivation method. In their experiments, Haemophilus influenzae (31.4%) was most commonly isolated from cases of tonsillar hypertrophy, followed by Streptococcus pyogenes (24.2%), Staphylococcus aureus (22.9%) and Streptococcus pneumoniae (12.6%). Similar to these earlier findings, Haemophilus influenzae was the dominant pathogen in the tonsillar hypertrophy group in our study. Jensen and colleagues  compared the microbiomes of tonsillar crypts in children and adults affected by recurrent tonsillitis with those of healthy adults and children with tonsillar hyperplasia. Twelve genera of microbial communities were identified in all samples regardless of age and health status. Notably, Haemophilus influenzae, Neisseria species and Streptococcus pseudopneumoniae were significantly more abundant in children. In our experiments, Haemophilus (23.5%) was the most prevalent bacterial species in tonsil tissue, followed by Fusobacterium (17.2%) and Streptococcus (8.3%). Haemophilus has thus been identified as the most common bacterium, not only in recurrent tonsillitis but also hypertrophic tonsil samples [9, 13, 27, 28].
Haemophilus and Staphylococcus are the representative microbiota in both tonsillectomized and non-tonsillectomized children with recurrent and tonsillar hypertrophy [27, 28]. Haemophilus was identified as the most common bacterial species in both saliva and tonsils in our study (Table 3). However, Staphylococcus, which is frequently detected in tonsillitis, was less abundant in our study and weakly correlated between the groups (data not shown). Specifically, Staphylococcus was ranked as 22 in terms of abundance in tonsils and 38 in saliva. The low abundance of Staphylococcus may be explained by the fact that the bacterium is mainly associated with inflammation and we selected only hyperplasia cases for analysis (except tonsillitis).
According to Kuhn et al. , the predominant aerobic and facultative organisms were Haemophilus influenzae, followed by Neisseria and Staphylococcus aureus, in pediatric patients subjected to tonsillectomy, while the predominant anaerobic bacteria were Fusobacterium, Bacteroides and Prevotella melaninogenica. These earlier results were identical to our finding that Haemophilus, Fusobacterium, Prevotella, and Neisseria are among the top 10 ranked genera in tonsil and saliva samples of Korean children (Table 3). Among the top 10 microbiota identified in the current study, the dominant bacteria in both saliva and tonsils were facultative anaerobes. Moreover, the most abundant species in saliva (Streptococcus of Firmicutes phylum) and tonsils (Haemophilus of Proteobacteria phylum) were consistent with previous reports [10, 25, 26, 29].
The top 10 ranked microbes in our study mostly showed a strong positive correlation within the saliva microbiome while both positive and negative correlations were detected within the tonsil microbiome. These differences could be explained as follows: since saliva is continuously secreted and renewed in the oral cavity, the microbial community is relatively constant. Compared with saliva, microbiota in tonsils are exposed to an environment with insufficient supply of oxygen and nutrients due to stagnation in the crypt, potentially leading to competition between two microbes and, consequently, negative correlation.
Treponema 2 showed the most significant positive correlation between saliva and tonsil environments (Fig. 4), although its abundance was low (1.3% in saliva, 3.2% in tonsils; Table 3). The genus Treponema contains both pathogenic (treponematoses) and non-pathogenic species. Non-pathogenic treponemes form part of the normal microbial flora of the oral cavity, intestinal tract or genital tract. A number of oral treponemes have been associated with gingivitis and periodontal disease . Prevotella 7 in tonsils was positively correlated with Streptococcus, Veillonella, and Alloprevotella in saliva. Prevotella 7 is one of the major periodontal disease- causing microbiota, such as periodontitis and gingivitis. Furthermore, Streptococcus in saliva, which affects Prevotella 7 in tonsils, triggers airway infections, such as pharyngitis, tonsillitis, and tympanitis. This strong positive correlation supports a link between dental inflammatory and oropharyngeal inflammatory diseases, further indicating a significant connection between the oral cavity (saliva) and oropharynx (tonsil).
Based on the collective results, we have expanded our understanding of the interactions between microbiomes of saliva and tonsils. Several factors are responsible for loss of microbial diversity and homeostatic function, including inflammation, diet, xenobiotics, and altered host cell function . Given its critical roles in multiple diseases, the microbiome has become an extremely attractive target for therapeutic interventions . One of the biggest hurdles in microbiome research is identification of cause-effect relationships and designing of microbiome-based therapies that are able to achieve predictable outcomes on the microbial community and host cell function. Further studies are required to develop strategies aimed at modulating the microbiome profile for improvement of host function and health.