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ISSN : 1226-7155(Print)
ISSN : 2287-6618(Online)
International Journal of Oral Biology Vol.49 No.4 pp.100-109
DOI : https://doi.org/10.11620/IJOB.2024.49.4.100

Comparison of oral microbiome diversity between children and young adults in Korea

Jung Hwa Park1, Si Yeong Kim1,2, Jin Chung1,2, Hee Sam Na1,2*
1Department of Oral Microbiology, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea
2Oral Genomics Research Center, Pusan National University, Yangsan 50612, Republic of Korea
*Correspondence to: Hee Sam Na, E-mail: heesamy@pusan.ac.krhttps://orcid.org/0000-0002-3246-4681
December 15, 2024 December 9, 2024 December 10, 2024

Abstract


The oral microbiome plays a vital role in maintaining oral and overall health and affects immune responses, digestion, and pathogen suppression. While most studies focus on age groups prone to specific conditions, such as dental caries in children or periodontal disease in older adults, limited data exist on preschool-aged children and young adults. This study investigates the composition and diversity of the oral microbiome between these age groups for enhanced understanding of a healthy oral microbiome. Microbial samples from the supragingival regions of 41 children and 31 young adults in Korea were analyzed using 16S rRNA gene sequencing. Alpha and beta diversity were assessed, and linear discriminant analysis effect size (LEfSe) identified taxa with significant differences in abundance between the groups. No significant differences in alpha diversity were observed between children and young adults however, beta diversity analysis revealed notably compositional differences. At the phylum level, Firmicutes were more abundant in children, whereas Actinobacteria were more prevalent in young adults. Genera such as Veillonella and Lautropia were more abundant among children, whereas Haemophilus and Rothia were more common among young adults. LEfSe analysis identified Veillonella rogosae and Lautropia mirabilis as more abundant in children, whereas Haemophilus parainfluenzae and Rothia dentocariosa were more prevalent in young adults. The observed differences suggest that children’s microbiomes are associated with biofilm development, while young adults’ microbiomes involve biofilm maturation and immune modulation. These findings highlight the age-related shift in oral microbiome composition, emphasizing the importance of monitoring these changes to support long-term oral health.


Microbiota , Next-generation sequencing , Young adult , Children

초록

    © The Korean Academy of Oral Biology. All rights reserved.

    This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Introduction

    The oral microbiome plays a critical role in maintaining oral and overall health [1,2]. As a diverse community of microorganisms, it interacts with the teeth, gums, and mucosal surfaces, contributing to functions such as food digestion, immune responses, and the suppression of pathogens [3]. Disruptions in this microbial balance can lead to a variety of oral diseases, including dental caries, periodontitis, gingivitis, and oral cancer [4]. Additionally, the oral microbiome has been associated with systemic diseases such as cardiovascular disease, diabetes, and respiratory conditions [5,6].

    Established immediately after birth, the oral microbiome and its ecosystem evolve with tooth development and age, and play a crucial role in both oral and systemic health [7]. Initially, various microbial species are introduced through environmental factors such as the mode of delivery and feeding practices [7,8]. As primary teeth develop, the composition of the oral microbiome changes significantly, and further changes continue to occur when primary teeth are replaced by permanent teeth [9,10]. In adulthood, the oral microbiome maintains a relatively stable state [11]. However, aging leads to changes in its composition due to factors like decreased immune function, reduced salivary flow, and medication use, increasing the risk of pathogenic bacterial proliferation and oral diseases [11,12]. Studying a healthy oral microbiome is essential, as it establishes a baseline for identifying age-specific variations and understanding changes that may lead to disease [13]. However, most studies have focused on children around age 10, where dental caries prevalence is high [14,15], and on middle-aged to older adults, where periodontal diseases and oral cancer rates are more prevalent [16,17].

    Next-generation sequencing (NGS) has enabled the simultaneous parallel analysis of hundreds of thousands of specific genes, such as 16S ribosomal RNA (rRNA), by amplification through polymerase chain reaction (PCR). This technology has led to significant advancements in the examination and comprehension of the oral microflora [18]. Despite extensive research, there is limited data comparing healthy oral microbiota between different age groups, particularly in preschool-aged children and young adults in their early 30s. In this study, we analyzed microbial samples collected from the supragingival regions of children and young adults. Following the sequencing of the V3–V4 variable region of 16S rRNA using NGS, we assessed the differences in microbial diversity. This study may contribute to understanding age-specific composition and potential shifts between these life stages.

    Materials and Methods

    1. Study samples

    The child participants were drawn from kindergarten in Yangsan City. The young adult participants were drawn from Pusan National University Dental Hospital (Yangsan, Korea). The subjects underwent a clinical oral examination by professional dentist. Microbial samples from the supragingival sites were by swabbing with a sterile micro-brush and were then placed into individual sterile 1.5-mL microcentrifuge tubes. All subjects gave written consent to participate in this study, and the protocol was approved by the Institutional Review Board of Pusanl National University (IRB No.: PNU-2018_49_HR, PNUDH-2017-023).

    2. Extraction of genomic DNA and NGS analysis

    Total DNA was extracted from each sample using a Gram Positive DNA Purification Kit (Lucigen, Biosearch Technology) following the manufacturer’s instructions. The final concentration was measured using a NanoDrop ND-1000 Spectrophotometer (Thermo Fisher Scientific) and stored at –80°C until use. PCR amplification of the V3–V4 fragment of the 16S rRNA gene was carried out. Purified amplicons were pooled in equimolar amounts and subjected to paired-end sequencing with HiSeq 2500 (Illumina). Raw fastq files were demultiplexed and processed using tools available in QIIME2 (version 2019.7). Reads were demultiplexed with q2-demux, and quality filtered and dereplicated with q2-dada2. Representative sequence sets for each dada2 sequence variant were used for taxonomy classification using the naive Bayes machine learning classifier [19].

    3. Microbiome analysis and statistical analysis

    Basic microbiome analyses have been performed using the QIIME2 (version 2023.9) and associated plugins [20]. To measure alpha diversities, Choa1 index and Shannon’s index method were used. Beta diversity was assessed by calculating distance matrices based on Bray–Curtis distances and visualized by Principal Coordinates Analysis (PCoA). The Mann–Whitney U test and the non-parametric permutation multivariate analysis of variance (PERMANOVA) tests were used to assess the statistical significances for alpha and beta diversities, respectively. To assign taxonomy to the unique representative sequences, pre-trained Naive Bayes classifier, using ribosomal database project database was used. To test differential abundance of bacterial species, linear discriminant analysis effect size (LEfSe) was applied with default settings [21].

    Results

    1. Sample characterization

    In total, 72 subjects, including 41 children (Child) and 31 young adults (Young_Adult), were recruited. The demographic parameters of the participants are summarized in Table 1.

    2. Diversity and abundance of the microbiome

    Alpha diversity, represents the microbial diversity within individual samples, was measured using the Chao1 and Shannon indices. Based on these measures, no significant differences were detected between the Child group and the Young_ Adult group (Fig. 1A). The beta diversity, which represents the microbial diversity between groups, was evaluated using the Bray–Curtis dissimilarity metric. A significant difference between the Child group and the Young_Adult group was observed, with a p-value of 0.0001 from the PERMANOVA test (Fig. 1B). Taken together, the richness and evenness within each group was similar between Child and Young_Adult while overall microbial composition was significantly different.

    After taxonomic assignment, the average relative abundance of each taxon was calculated. At the phylum level, the five most abundant phyla were Firmicutes, Proteobacteria, Actinobacteria, Fusobacteria, and Bacteroidetes. Actinobacteria were more abundant in the Young_Adult group (25.8%) compared to the Child group (15.9%), while Firmicutes were more abundant in the Child group (48.7%) than in the Young_Adult group (40.3%). Both Bacteroidetes and Fusobacteria were less abundant in the Young_Adult group (2.5%, 4.9%, respectively) compared to the Child group (4.3%, 5.9%, respectively) (Fig. 2A).

    At the genus level, among the 20 most abundant genera detected. The most abundant genera in the Child group samples included Streptococcus and Veillonella, which constituted more than 43% of the total sequences. In the Young_Adult group samples, Streptococcus , Rothia , and Haemophilus represented more than 62% of the total sequences. In the analysis of genera distribution, several genera were more abundant in the Child group compared to the Young_Adult group, including Veillonella (Child vs. Young_Adult: 11.27% vs. 8.14%), Lautropia (7.90% vs. 4.30%), Gemella (2.61% vs. 0.83%), and Campylobacter (0.91% vs. 0.51%). In contrast, genera such as Haemophilus (16.66% vs. 6.01%), Rothia (15.73% vs. 4.86%), Prevotella (0.38% vs. 0.66%), and Actinomyces (7.72% vs. 8.48%) were more prevalent in the Young_Adult group (Fig. 2B).

    The species level analysis demonstrates marked differences between the Child and Young_Adult groups. In the Child group, Lautropia mirabilis (7.91% vs. 4.30%), Veillonella parvula (3.46% vs. 1.19%), and Gemella (2.28% vs. 0.71%) were more abundant. Streptococcus was dominant in both groups, comprising 30.78% in the Child group and 26.27% in the Young_Adult group. Interestingly, some species such as Streptococcus salivarius was more prevalent in the Young_Adult group (0.31% vs. 3.16%). Additionally, Rothia species, including Rothia aeria (2.02% vs. 2.87%), Rothia dentocariosa (2.40% vs. 11.51%), and Rothia mucilaginosa (0.44% vs. 1.35%), were more abundant in the Young_Adult group, along with Haemophilus parainfluenzae (5.44% vs. 15.97%) (Fig. 2C).

    3. Comparison of the diversity of microorganisms between samples

    Next, LEfSe was applied to evaluate the differences in relative abundances between the Child and Young_Adult groups. When the species abundances were compared, H. parainfluenzae, R. dentocariosa, Lactobacillus salivarius, Saccaribacteria (TM7) HMT_346, and Aggregatibacter sp. HMT_458 were the top 5 taxa in the Child group, while L. mirabilis , Abiotrophia defectiva, Leptotrichia hongkongensis, Capnocytophaga sputigena, and Actinomyces gerencseriae were the top 5 in the Young_Adult group (Fig. 3). When Tukey’s post hoc test was applied to further evaluate their significance, the abundance of Veillonella rogosae, L. mirabilis, and Gemella morbillorum was found to be significantly higher in the Child group. In contrast, the abundance of Actinomyces naeslundii, H. parainfluenzae, R. dentocariosa, Prevotella nigrescense, Porphyromonas endodontalis, Porphyromonas pasteri, and S. salivarius were found to be significantly higher in the Young_Adult group (Fig. 4). These results highlight the distinct microbial composition between the Child and Young_Adult groups, indicating age-related shifts in oral microbiome diversity.

    Discussion

    As the oral microbiome plays a critical role in maintaining both oral and overall health, research on disruptions in microbial balance has primarily focused on oral diseases, including dental caries, periodontitis, gingivitis, and oral cancer [1,2,4]. Studies on dental caries have mainly targeted age groups with high prevalence, around age 10. Research on periodontitis, gingivitis, and oral cancer has predominantly focused on midadults to the elderly. Consequently, there is limited data on microbiome composition in kindergarten-aged children and young adults in their early 30s. In this study, microbial samples were collected from children and young adults to characterize their compositional features.

    Alpha diversity was estimated to evaluate the richness and evenness of the microbiome [22]. The Chao1 index, which represents community richness, showed no significant difference between the Child group and the Young_Adult group. Similarly, the Shannon index, representing both richness and evenness of the microbial community, also showed no significant difference between the two groups (Fig. 1A). Thus, the diversity of microbial species in terms of quantity and distribution was comparable across these age groups. However, the significant differences observed in beta diversity (Fig. 1B) suggest that the microbial composition across groups was notably distinct. These findings support previous observations of agerelated differences in oral microbiota composition as individuals transition from childhood to adulthood [10].

    When the average relative abundance of each taxon was plotted at the phylum level, Firmicutes was more prevalent in the Child group, accounting for approximately 40% of the total abundance, whereas Actinobacteria was more abundant in the Young_Adult group. Firmicutes include various bacteria that play essential roles in maintaining the stability and resilience of the oral microbiome, often aiding in processes like carbohydrate metabolism and pH balance, which are crucial for oral health [1,23]. Members of this phylum, such as Streptococcus and Veillonella, are key players in early biofilm formation and contribute to creating a balanced microbial environment [24]. Actinobacteria are similarly vital for oral health, as this phylum encompasses bacteria like Rothia that are involved in nitrogen metabolism and help regulate immune responses in the oral cavity [25,26]. Some Actinobacteria members are known to have protective effects, though shifts in their abundance may also be associated with oral diseases under certain conditions [12]. In addition, the Proteobacteria phylum, which had similar relative abundances, revealed distinct patterns at the genus level. Specifically, Haemophilus, more commonly associated with the maturation of the oral microbiome, was more abundant in the Young_Adult group, while Neisseria, known to be initial colonizers in biofilms, was more abundant in the Child group [27,28]. Understanding these microbial shifts can provide insights into the development of age-specific strategies for preventing oral diseases.

    A large of number of significant taxa was determined at species level. The notable species-level differences observed between the Child and Young_Adult groups suggest important functional roles linked to the development and maturation of the oral microbiome (Figs. 3 and 4). In the Child group, L. mirabilis, V. rogosae, and G. morbillorum were among the significant species found. L. mirabilis is commonly found in the oral cavity, particularly within the subgingival microbiome, and is generally associated with periodontal health. Studies have shown its presence in healthy individuals as well as in immunocompromised populations, where it appears as a nonpathogenic commensal bacterium [29]. Studies comparing subgingival microbial profiles indicate that L. mirabilis is more prevalent in healthy periodontal conditions than in advanced or severe periodontitis, supporting its role in maintaining a balanced and stable oral microbiome [30,31]. V. rogosae plays a crucial role in lactate fermentation and pH regulation, which is particularly relevant in children with high sugar diets [32]. This species helps mitigate acid production, potentially reducing the risk of dental caries by consuming lactate produced during carbohydrate metabolism [33]. G. morbillorum plays an important role in early oral biofilm formation, contributing to the initial biofilm structure through coaggregation with other oral bacteria. Specifically, it interacts with early colonizers, including Streptococcus spp., to stabilize the developing biofilm, creating an environment that facilitates the subsequent attachment of other bacteria [24]. It is generally found as a commensal bacterium in healthy oral microbiomes. Neisseria species were more common in the Child group, consistent with their role as early colonizers in biofilm formation, which may be indicative of the dynamic biofilm development in individuals [34]. The higher abundance of these early colonizers in children suggests active biofilm formation and microbial succession occurring during the initial stages of oral microbiome maturation.

    In the Young_Adult group, the abundance of A. naeslundii, H. parainfluenzae, R. dentocariosa, Prevotella histicola, S. salivarius , P. pasteri , and P. endodontalis were significantly higher. A. naeslundii plays an essential role in the early formation of dental plaque by adhering to tooth surfaces and coaggregating with other oral bacteria, thereby facilitating biofilm development [24]. This bacterium is commonly found in the normal bacterial flora of healthy oral cavities but has also been observed in individuals with localized aggressive periodontitis, suggesting that it may play a dual role depending on the microbial environment [35,36]. H. parainfluenzae is also abundant in the dental plaque of healthy individuals, indicating its role as a commensal bacterium in the oral cavity [37-39]. Although it can act as an opportunistic pathogen under certain conditions, H. parainfluenzae is generally associated with a balanced oral microbiome and is thought to exert beneficial immunomodulatory effects, helping to maintain immune homeostasis [40,41]. R. dentocariosa is generally regarded as a commensal bacterium associated with oral health. However, it has sometimes been found in elevated levels in periodontal pockets, suggesting it may coexist with pathogens in dysbiotic conditions [42]. P. histicola is a member of the healthy oral microbiome and is involved in carbohydrate fermentation, helping to maintain metabolic balance in the oral cavity and creating an environment that supports the growth of other commensal bacteria [43,44]. S. salivarius is another key member of the healthy oral microbiome, primarily known for its probiotic properties and can produce bacteriocins, which inhibit the growth of pathogenic bacteria, thus helping to maintain a balanced microbial environment in the oral cavity [45]. The prevalence of bacteria such as A. naeslundii, H. parainfluenzae, R. dentocariosa, P. histicola, and S. salivarius in the Young_Adult group suggests that the oral microbiome becomes more stable and mature with age. This shift may enhance immune modulation and inflammation control, contributing to overall oral health.

    In addition, immune modulation effect has been noted in above mentioned species. R. dentocariosa contributes to immune modulation by inducing TNF-α production via TLR2, helping maintain mucosal homeostasis [46]. P. histicola is also known to modulate inflammation by inhibiting pro-inflammatory cytokine production through the Th1, Th2, and Th17 pathways, while promoting anti-inflammatory cytokines such as IL-10, which may contribute to immune homeostasis and alleviation of autoimmune disease symptoms [47]. S. salivarius has been reported to alleviate inflammatory responses by inhibiting the production of pro-inflammatory cytokines. Notably, it is known to reduce the expression of inflammatory mediators, such as TNF-α and IL-6, induced by periodontal pathogens like Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans , thereby regulating immune responses and suppressing excessive immune activation [48,49]. Thus, oral bacteria found in young adults may contribute to immune modulation supporting immune homeostasis and potentially alleviating inflammatory responses.

    Interestingly, several potential periodontopathogens were also found in young adults. P. endodontalis is a recognized contributor to inflammatory conditions within the oral cavity, particularly in chronic periodontitis, where it is commonly found in elevated levels within periodontal pockets and is associated with tissue degradation through its pro-inflammatory activity [50]. P. pasteri is less well studied and lacks direct evidence linking it specifically to inflammatory periodontal disease. However, as a member of the Porphyromonas genus, P. pasteri may still influence the composition and balance of the salivary and periodontal microbiomes, potentially affecting overall oral health [51,52]. These bacteria do not constitute a large proportion of the overall abundance in the oral microbiome (Fig. 2C). However, there is a noticeable increase in their abundance in the Young_Adult group compared to the Child group (Figs. 3 and 4). This shift suggests an age-related increase in the prevalence of periodontal inflammation, indicating that the frequency of inflammatory changes may rise gradually with age.

    This study complements existing data by analyzing and comparing the oral microbiome composition of kindergartenaged children and young adults in their 20s, a relatively underexplored age range. The results reveal that while there is no significant difference in microbiome diversity between children and young adults, there are meaningful differences in microbiome composition. Reflecting the maturation of the oral microbiome with age, the Child group showed an abundance of species involved in early biofilm formation, whereas the Young_Adult group exhibited an increase in species contributing to inflammation regulation in the oral cavity. However, this study primarily focused on the supragingival region for microbiome sampling, which may be insufficient to fully describe the diverse microbial communities present in other oral sites, such as the tongue, saliva, and subgingival areas [25,38]. Additionally, the cross-sectional design and relatively small sample size limit the generalizability of the findings. Longitudinal studies with more comprehensive sampling across various oral regions are necessary to validate the observed age-related microbial changes and provide deeper insights into their implications for oral health over time. Furthermore, certain microbial species found to be more prevalent in the Young_Adult group may not present clinical issues in the 20s but could serve as early indicators of an elevated risk for periodontal disease after the age of 40s. In fact, periodontal disease incidence tends to increase sharply after 40, suggesting a gradual increase in inflammatory disease risk as the oral microbiome shifts with age [53,54]. Therefore, the observed changes in oral microbiome composition from childhood to young adulthood underscore not only age-related microbial shifts but also the importance of regular monitoring and oral care from an early age. Early management can play a significant role in maintaining long-term oral health, particularly in preventing oral diseases such as periodontal disease.

    Funding

    This work was supported by a 2-Year Research Grant of Pusan National University.

    Conflicts of Interest

    No potential conflict of interest relevant to this article was reported.

    Figure

    IJOB-49-4-100_F1.gif

    Bacterial community comparisons depending on sampling sites. (A) Alpha diversity was used to describe the microbial richness and evenness within samples using the Chao1 and Shannon index. Statistical significance was assessed using the Mann–Whitney U test. (B) Beta diversity of supraginigival microbiome. Principal Coordinate Analysis (PCoA) of the Bray–Curtis distance was performed to determine the microbial community structure, with statistical significance tested using PERMANOVA.

    PERMANOVA, permutation multivariate analysis of variance.

    IJOB-49-4-100_F2.gif

    Relative abundance of bacterial species. (A) Phylum level, (B) genus level, and (C) species level.

    IJOB-49-4-100_F3.gif

    Comparisons of the taxa showing significant difference between children and young adults. Linear discriminant analysis (LDA) and effect size analysis (LEfSe) was performed for the analysis. LDA score > 2.5 was plotted, and statistical significance was determined using the default settings in LEfSe.

    IJOB-49-4-100_F4.gif

    Relative abundance of selected significant bacterial species. Statistical significance was assessed using the Mann–Whitney U test with a threshold of p < 0.05.

    Table

    Demographic features of the participants

    Values are presented as mean ± standard deviation or number.

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