High-throughput sequencing identifies salivary microbiota in Chinese caries-free preschool children with primary dentition

High-throughput sequencing identifies salivary microbiota in Chinese caries-free preschool children with primary dentition 无龋学龄前乳牙列儿童唾液微生物群落研究 1 first-author https://orcid.org/0000-0001-6460-9577 1 1 1 1 2 1 corresp Shuli DENG, dengshuli@zju.edu.cn Shuli DENG, dengshuli@zju.edu.cn dengshuli@zju.edu.cn The Affiliated Hospital of Stomatology, School of Stomatology, Zhejiang University School of Medicine, and Key Laboratory of Oral Biomedical Research of Zhejiang Province, Hangzhou310006, China The Affiliated Hospital of Stomatology, School of Stomatology, Zhejiang University School of Medicine, and Key Laboratory of Oral Biomedical Research of Zhejiang Province, Hangzhou310006, China Department of Stomatology, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang471009, China Department of Stomatology, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang471009, China Objectives 2 The study aimed at identifying salivary microbiota in caries-free Chinese preschool children using high-throughput sequencing. Methods 2 Saliva samples were obtained from 35 caries-free preschool children (18 boys and 17 girls) with primary dentition, and 16S ribosomal DNA (rDNA) V3–V4 hypervariable regions of the microorganisms were analyzed using Illumina MiSeq. Results 2 At 97% similarity level, all of these reads were clustered into 334 operational taxonomic units (OTUs). Among these, five phyla (Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Candidate division TM7) and 13 genera ( Streptococcus , Rothia , Granulicatella , Prevotella , Enterobacter , Veillonella , Neisseria , Staphylococcus , Janthinobacterium , Pseudomonas , Brevundimonas , Devosia , and Gemella ) were the most dominant, constituting 99.4% and 89.9% of the salivary microbiota, respectively. The core salivary microbiome comprised nine genera ( Actinomyces , Capnocytophaga , Gemella , Granulicatella , Lachnoanaerobaculum , Neisseria , Porphyromonas , Rothia , and Streptococcus ). Analysis of microbial diversity and community structure revealed a similar pattern between male and female subjects. The difference in microbial community composition between them was mainly attributed to Neisseria ( P =0.023). Furthermore, functional prediction revealed that the most abundant genes were related to amino acid transport and metabolism. Conclusions 2 Our results revealed the diversity and composition of salivary microbiota in caries-free preschool children, with little difference between male and female subjects. Identity of the core microbiome, coupled with prediction of gene function, deepens our understanding of oral microbiota in caries-free populations and provides basic information for associating salivary microecology and oral health. 目的 2 利用高通量测序技术对未罹患龋病的学龄前儿童口腔内的唾液微生物群落进行鉴定，从而揭示其群落组成及结构特征。 创新点 2 揭示了学龄前乳牙列儿童唾液微生物群落组成及结构特征，为进一步研究口腔微生态与儿童健康或疾病之间的关系奠定了基础。 方法 2 我们采集了来自35名无龋学龄前乳牙列儿童的口腔唾液样本，其中包括18名男孩和17名女孩。利用Illumina MiSeq平台对微生物样本的16S rDNA V3-V4高变区进行测序分析，从而分析获得的微生物群落特征。 结论 2 唾液微生物群落中的优势物种分布于5个门及13个属微生物种类中；性别因素不造成唾液微生物群落组成和结构的显著差异；通过功能预测分析，微生物群落中含量最丰富的基因所代表的功能和途径是氨基酸的转运和代谢。 Salivary microbiota Caries-free Preschool children Primary dentition Illumina MiSeq 16S rDNA V3-V4 hypervariable regions 唾液微生物群落 无龋 学龄前儿童 乳牙列 MiSeq测序 16S rDNA V3-V4高变区 1 Introduction 1 The human body is considered a superorganism, with a combination of microbial and human attributes ( Gillet al., 2006 Gill et al., 2006 ). Various microorganisms have been found residing in different parts of the human body, at an estimated ratio of 1:1 to the number of human cells ( Senderet al., 2016 Sender et al., 2016 ). Previous studies have successfully characterized human microbiota, and reported that the microorganisms play an important role in host homeostasis ( Ismailet al., 2009 Ismail et al., 2009 ; Kauet al., 2011 Kau et al., 2011 ) as well as health ( Tappenden and Deutsch, 2007 Tappenden and Deutsch, 2007 ; Cogenet al., 2008 Cogen et al., 2008 ). Advances in metagenomics, coupled with the rise of personalized and precise medicine concepts, have generated vital data on human microbiota, which is expected to improve medical therapies. The oral cavity, which harbors about 1000 microbial species, is one of the most diverse and important habitats for human microorganisms ( Wade, 2013 Wade, 2013 ). The mouth of a healthy individual harbors relatively stable microbial communities, with disturbances in these communities implicated in a series of local and systemic infectious diseases, including caries, periodontal, liver, and inflammatory bowel diseases ( Liuet al., 2012 Liu et al., 2012 ; Saidet al., 2014 Said et al . , 2014 ; Struzycka, 2014 Struzycka, 2014 ; Linget al., 2015 Ling et al . , 2015 ). Studies on oral microbiota in children have revealed the changes in microbial communities that are associated with oral diseases, including dental caries, periodontitis, and pigmentation ( Jianget al., 2013 Jiang et al., 2013 ; Campiscianoet al., 2017 Campisciano et al . , 2017 ; Zhanget al., 2017 Zhang et al., 2017 ). However, only a handful of studies have reported on the oral microbiota in caries-free children ( Xinet al., 2013 Xin et al., 2013 ; Leeet al., 2016 Lee et al., 2016 ). Furthermore, the identity and function of microorganisms in the saliva of caries-free preschool children in China remain unclear. Approximately half of all detected oral microorganisms are unculturable ( Dewhirstet al., 2010 Dewhirst et al., 2010 ), posing a big challenge for researchers seeking to analyze oral microbiota comprehensively. However, this challenge can be overcome by using culture-independent sequencing techniques which identify microorganisms based on hypervariable regions in the 16S ribosomal DNA (rDNA) gene ( Yeet al., 2012 Ye et al., 2012 ). Recent advances in high-throughput sequencing have enhanced studies into oral microbiota. These techniques collect more information, with a higher efficiency as well as a greater width of coverage, by allowing the detection not only of dominant microbial taxa, but also of rare and unknown microorganisms when compared to traditional sequencing ( Bennet al., 2018 Benn et al., 2018 ). However, to the best of our knowledge, a very limited number of studies have used high-throughput sequencing to identify the oral microbiota of primary dentition in caries-free preschool children. Therefore, this study aimed at identifying the composition and gene functions of salivary microbiota of primary dentition in caries-free Chinese preschool children using Illumina MiSeq high-throughput sequencing, targeting the 16S rDNA V3–V4 hypervariable regions. Furthermore, we compared the results from male and female subjects to reveal the effects of gender on the diversity, composition, and structure of salivary microbiota. Our findings are expected to provide new insights into the composition and structure of the oral microbiome and reveal its relationship with health in preschool-age children. In future, the resulting microbial data are expected to be applied in the assessment of oral health and in disease diagnosis and prognosis. 2 Results 1 2.1 Demographic characteristics 2 The demographic characteristics of the 35 subjects (18 boys and 17 girls) recruited are summarized in Table 1 Table 1 . Their average age was (48.4±3.1) months, with no statistical differences ( P =0.640) observed between males and females with regard to age. a No statistical differences ( P =0.640) observed between males and females according to Student’s t -test. Data are expressed as number or mean±standard deviation (SD). Group Number of subjects Age (month) Male 18 48.6±3.0 a Female 17 48.2±3.3 a Total 35 48.4±3.1 2.2 Sequencing output and microbial composition 2 A Good's coverage index of more than 0.999 was obtained for each sample, indicating that the identified sequences represented a vast majority of the microorganisms in these saliva samples. Rarefaction curves indicated that the sequencing technique used effectively revealed complete bacterial richness ( Fig. 1 Fig. 1 ). These two indicators combined suggested that the sequencing depth was sufficient and that additional sampling was not necessary. High-throughput Illumina MiSeq sequencing revealed a total of 1 824 344 reads, among which 1 712 531 (93.9%) high-quality reads passed quality control and were therefore selected for further analysis. An average of 48 929 reads per sample (standard deviation (SD)=5706; range: 39 624‒58 231) were obtained, and all reads were found to be over 420 bp, with an average length of (446.5±4.2) bp. At the 97% similarity level, all reads were clustered into 334 OTUs, which represented 13 phyla, 24 classes, 40 orders, 63 families, and 113 genera. Five phyla, including Firmicutes (73.4%), Proteobacteria (12.8%), Actinobacteria (7.3%), Bacteroidetes (4.4%), and Candidate division TM7 (1.5%), were the most dominant across all studied subjects. These constituted 99.4% of the entire salivary microbiota, whereas other phyla were detected in relatively low proportions ( Fig. 2 Fig. 2 a). At the genus level, 13 genera, including Streptococcus (59.8%), Rothia (6.7%), Granulicatella (5.1%), Prevotella (3.6%), Enterobacter (3.6%), Veillonella (2.2%), Neisseria (1.5%), Staphylococcus (1.3%), Janthinobacterium (1.3%), Pseudomonas (1.3%), Brevundimonas (1.2%), Devosia (1.2%), and Gemella (1.1%) were dominant. These represented 89.9% of all salivary microbiota ( Fig. 2 Fig. 2 b). 2.3 Microbial diversity and overall community structure between male and female subjects 2 There were no statistically significant differences between male and female subjects with regard to Ace, Chao, Shannon, and Simpson α diversity indices ( Table 2 Table 2 ; Fig. S1). This implied that gender had no effect on the richness or evenness of salivary microbiota populations in preschool children with caries-free primary dentition. * Mann-Whitney U -test. Data are expressed as mean±standard deviation. Group Ace Chao Shannon Simpson Male ( n =18) 143.0±40.0 138.8±39.6 1.49±0.49 0.431±0.158 Female ( n =17) 149.0±33.2 146.4±36.8 1.64±0.60 0.358±0.142 P -value * 0.597 0.409 0.391 0.146 The high similarity of salivary microbial communities between male and female subjects was also reflected in the Venn diagram (Fig. S2). In summary, there was a high overlap in OTUs between the groups. Specifically, a total of 265 OTUs were shared by both groups of subjects, accounting for 90.8% and 86.3% of their total OTUs, respectively. In addition, 27 and 42 exclusive OTUs were found in male and female subjects, respectively, accounting for a respective 9.2% and 13.7% of their total OTUs. No clear separation of clusters was revealed by a principal coordinate analysis (PCoA)-based comparison of overall microbial community structure between male and female groups, using weighted UniFrac distance measurements (β diversity; Fig. 3 Fig. 3 ). The dissimilarity test corroborated the PCoA results, revealing no marked difference between these two groups (analysis of similarities (ANOSIM) test, R =-0.0189, P =0.78). These results indicated that gender did not significantly affect the structure of salivary microbial communities. 2.4 Taxonomic differences between male and female subjects 2 As mentioned earlier, a total of 13 phyla were identified, among which five (Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Candidate division TM7) were dominant in both male and female subjects, albeit at no statistical difference (Fig. S3). In addition, Deinococcus-Thermus was only found in female subjects. No intergroup differences were observed among the rest of low-abundance phyla. At the genus level, we detected 113 genera, 13 of which were dominant. Among these, only Neisseria showed a statistical difference between the groups: a significantly higher abundance ( P =0.023) of this genus was observed in male than in female subjects ( Fig. 4 Fig. 4 ). No statistical differences were observed between the groups with regards to the other low-abundance genera. 2.5 Salivary core microbiome in caries-free preschool children 2 A core microbiome is defined as a set of microbial taxa shared by all subjects ( Turnbaughet al., 2007 Turnbaugh et al., 2007 ). In the present study, analysis of the salivary core microbiome from caries-free preschool children with primary dentition revealed a decrease in the number of core OTUs with increase in sample size. Specifically, the number of shared OTUs first decreased to 10 when the sampling number reached 32, and then remained constant ( Fig. 5 Fig. 5 ). This result highlighted the importance of sample size (at least 32 in this study) for the reliable definition of a core microbiome. At the phylum level, we detected four phyla across all subjects, including Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. However, a total of nine genera, including Actinomyces , Capnocytophaga , Gemella , Granulicatella , Lachnoanaerobaculum , Neisseria , Porphyromonas , Rothia , and Streptococcus , were detected in all subjects ( Table 3 Table 3 ). These genera accounted for 75.2% of all identified sequences, indicating that the core microbiome represents a significantly high proportion of total salivary microbiota in caries-free preschool children with primary dentition. Phylum Genus Actinobacteria Actinomyces Rothia Bacteroidetes Capnocytophaga Porphyromonas Firmicutes Streptococcus Gemella Granulicatella Lachnoanaerobaculum Proteobacteria Neisseria 2.6 Prediction of functions and pathways 2 OTU standardization using PICRUSt software revealed corresponding information on Clusters of Orthologous Genes (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO). We then compared the COG and KO information to datasets in the Evolutionary Genealogy of Genes Non-supervised Orthologous Groups (EggNOG) and KEGG databases to predict the corresponding functions (EggNOG) and pathways (KEGG) of all salivary microbial genes in caries-free primary dentition. Based on these analyses, the following represented the top ten functions and pathways: function unknown; amino acid transport and metabolism; general function prediction only; carbohydrate transport and metabolism; translation, ribosomal structure and biogenesis; transcription; replication, recombination and repair; cell wall/membrane/envelope biogenesis; inorganic ion transport and metabolism; and energy production and conversion. Conversely, chromatin structure and dynamics, RNA processing and modification, cytoskeleton, extracellular structure, and nuclear structure represented the five lowest functions and pathways ( Fig. 6 Fig. 6 ) . 3 Discussion 1 In 2007, the US National Institutes of Health (NIH) launched the Human Microbiome Project (HMP), a genetic engineering project aimed at characterizing human microbiota and ascertaining their effects on human physiology and pathology ( Petersonet al., 2009 Peterson et al., 2009 ). The oral cavity, which has a humid environment with a relatively constant temperature and contains a large variety of microorganisms (partly due to its various anatomical structures), is one of the most important microbial habitats. Previous studies have shown that the dominant microorganisms in saliva, which detach from oral tissues, are often commensal ( Pereiraet al., 2012 Pereira et al., 2012 ). For example, these microorganisms may originate from the biofilm matrix polymers degraded by bacterial enzymes, as well as from desquamation of epithelial surface cells ( Kaplanet al., 2004 Kaplan et al., 2004 ). Since diversity and composition of salivary microbiota are affected by interactions between microorganisms and their hosts, we believe that saliva may be an ideal medium for exploring oral microbiota. Studies on oral microbiota are challenging on the whole, due to the fact that about half of all oral microorganisms are unculturable. However, culture-independent approaches, such as high-throughput or next-generation sequencing, can be used to overcome this limitation ( Yeet al., 2012 Ye et al., 2012 ). In particular, the Illumina MiSeq platform is considered superior because it can provide a similar read length at a lower cost when compared to other related technologies such as Roche/454 pyrosequencing ( Fadroshet al., 2014 Fadrosh et al., 2014 ). In addition, previous studies have demonstrated the importance of different hypervariable regions (V1-V9) in the microbial 16S rRNA gene for species identification. The V3-V4 and V4-V5 regions have provided the most accurate results irrespective of the sequencing technology used ( Claessonet al., 2010 Claesson et al., 2010 ). Based on the aforementioned facts, we sought to identify and characterize salivary microbiota using Illumina MiSeq sequencing and targeting the V3-V4 regions. Microbial richness and diversity in the environment can be determined by single-sample diversity analysis (α diversity), including a series of statistical indices to estimate species abundance and diversity. As shown in Table 2 Table 2 , richness estimators (Chao and Ace) and diversity indices (Shannon and Simpson) were introduced for α diversity analysis in this study ( Ritz et al., 2020 Ritz et al., 2020 ; Zhou et al., 2020 Zhou et al., 2020 ). In addition, β diversity analysis can be used to determine similarity or difference in community structure among different groups. We carried out PCoA, a non-binding data dimension reduction analysis method based on the weighted UniFrac distance, to visualize the similarities in microbial structure among subjects. Specifically, we divided recruited subjects into two groups based on gender. α and β diversity indices revealed a high but not significantly different species richness, diversity, and community structure of salivary microbiota in the groups, indicating that gender does not affect these metrics. Furthermore, when it came to community composition of salivary microbiota, only genus Neisseria showed a higher relative abundance in the male group. The high similarities in these parameters suggest that the gender factor could be negligible for future investigations of salivary microbiota in primary dentition. Our results further revealed the relationship between sample size and overlapping species. Briefly, calculating species overlap is a reliable way of analyzing the presence of microbial communities in the oral cavity, although the core microbiome requires that core taxa be shared by all subjects. Generally, this enables screening out of transient bacteria to enable detection of only those with the most stable composition ( Belda-Ferreet al., 2012 Belda-Ferre et al., 2012 ; Grice and Segre, 2012 Grice and Segre, 2012 ). We found a distinct and rapid decline in overlapping species as the number of tested samples increased, although the number of overlapping species remained unchanged when the sample size exceeded 32. This implies that a small sample size is not enough to eliminate the contribution of transient bacteria to overlapping species, and a sample size of > 32 may be more reliable for defining a core microbiome. The oral core microbiome was first defined in 2009 using next generation sequencing, based on analysis of microorganisms residing in five different oral niches (dental surfaces, buccal mucosa, hard palate, tongue, and saliva) in three healthy adults ( Zauraet al., 2009 Zaura et al., 2009 ). Since then, numerous studies have demonstrated the existence of a core microbiome in the oral cavity, and some have revealed the core microbiome in dental plaque of caries-free children. Xin et al. (2013) Xin et al. (2013) analyzed the oral microbiota of ten caries-free Chinese children aged between six and eight years old, and found that 13 genera were shared by all subjects. Moreover, Lee et al. (2016) Lee et al. (2016) found ten conserved genera in all ten five-year old caries-free Korean preschool children. We detected nine genera in all subjects, with three of them ( Capnocytophaga , Neisseria , and Streptococcus ) being consistent with those in two previous studies. This suggests that these three genera are conserved in caries-free children in spite of the differences in oral niches, geography, and age. Previous studies have documented that some oral microorganisms are associated with certain oral diseases. For example, Streptococcus mutans is a well-known cariogenic species due to its high capacity for acid production and acid tolerance ( Loesche, 1986 Loesche, 1986 ). Surprisingly, we found a higher proportion of Streptococcus across all salivary microbiota detected in caries-free preschool children. We hypothesized that several species of Streptococcus were being detected, including S. mutans , Streptococcus sanguinis , Streptococcus oralis , Streptococcus sobrinus , and Streptococcus mitis , among others, which play distinct roles during the development of dental caries. Therefore, the high proportion of Streptococcus would not be considered an unhealthy status, because its existence cannot be simply equated to dental caries. Our findings are consistent with previous studies that found no statistical differences in Streptococcus between caries and caries-free populations ( Linget al., 2010 Ling et al., 2010 ; Xuet al., 2014 Xu et al., 2014 ; Jianget al., 2016 Jiang et al., 2016 ). As mentioned above, Capnocytophaga and Neisseria were conserved taxa in caries-free children. A previous study by Jiang et al. (2011) Jiang et al. (2011) reported a significantly higher frequency of Capnocytophaga in caries-free when compared to caries-susceptible Chinese preschool children. In addition, Anderson et al. (2016) Anderson et al. (2016) found that Neisseria was more prevalent in caries-free Swedish preschool children than in those with caries. Taken together, these findings indicate that Capnocytophaga and Neisseria may be important core microorganisms for children's dental health. Biomineralization, including remineralization, is a dynamic process that occurs in the hard tissue of teeth, such as enamel, dentin, and cementum. Teeth are at risk of demineralization throughout life because they are constantly exposed to food, drink, oral microbiota, and other substances ( Abou et al., 2016 Abou et al., 2016 ). Inorganic ions, including calcium, phosphate, and fluoride, play an important role in the battle between demineralization and remineralization processes and change the susceptibility of teeth to acid attack ( Hara and Zero, 2010 Hara and Zero, 2010 ; Lussiet al., 2012 Lussi et al., 2012 ; Meyeret al., 2018 Meyer et al., 2018 ). Biological apatite, such as hydroxyapatite, is an important inorganic component of dental hard tissues. Carbonates and other ionic substitutions can significantly disrupt the crystal structure in hydroxyapatite and weaken it, thereby increasing its solubility and susceptibility to dental caries ( Featherstone and Lussi, 2006 Featherstone and Lussi, 2006 ; Qu and Zhou, 2020 Qu and Zhou, 2020 ). The expansion of genes associated with carbohydrate transport and metabolism is an essential property for caries pathogens in the oral cavity. Taking S. mutans as an example, the transport of saccharides is primarily conducted by the activity of adenosine triphosphate (ATP)-binding cassette (ABC) transporters ( Tao et al., 1993 Tao et al., 1993 ; Webbet al., 2008 Webb et al., 2008 ), while the uptake and metabolism of saccharides are predominantly conducted by the activity of the phosphotransferase system (PTS) ( Vadeboncoeur and Pelletier, 1997 Vadeboncoeur and Pelletier, 1997 ; Deutscheret al., 2006 Deutscher et al., 2006 ). They are encoded in the genome of S. mutans and give it the ability to adjust carbohydrate metabolism in response to the changeable oral environment. In this study, we found that salivary microbiota genes were enriched in inorganic ion transport and metabolism and carbohydrate transport and metabolism. This implies that they may play an important role in the processes of demineralization and remineralization as well as organic acid production and tolerance, which can modify teeth's susceptibility to caries. Rudney et al. (2010) Rudney et al. (2010) performed a metaproteomic analysis of human salivary microbiota using three-dimensional peptide fractionation and tandem mass spectrometry, and found that the most predominant pathway/protein assignments within major COGs groups are the following: translation, ribosomal structure, and biogenesis; carbohydrate transport and metabolism; and amino acid transport and metabolism (which is consistent with our findings). However, our study is limited in its metagenomics scope; we have no information about metatranscriptomics. Previous study of gut microbiota revealed that quite a few microbial transcripts were not differentially expressed according to their genomic abundance. For instance, ribosomal biogenesis is genomically stable but differentially transcribed among subjects; ribosomal protein-coding genes tended to be overexpressed and variable at the RNA level, relative to their metagenomic abundance ( Franzosaet al., 2014 Franzosa et al., 2014 ). So, we believe that we need to fill this knowledge gap in our future research by incorporating the study of metatranscriptomics to elucidate oral microbial function. This study was also limited by a small sample size, which may have influenced microbial diversity to a certain extent. Furthermore, we did not collect dental plaque data, indicating the need for future studies to unravel more microbial information. 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BMC Microbiol , 9 : 259 . https://doi.org/10.1186/1471-2180-9-259 https://doi.org/10.1186/1471-2180-9-259 Defining the healthy "core microbiome" of oral microbial communities Zhang F , Li Y , Xun Z , et al. , 2017 . A preliminary study on the relationship between iron and black extrinsic tooth stain in children . Lett Appl Microbiol , 64 ( 6 ): 424 - 429 . https://doi.org/10.1111/lam.12728 https://doi.org/10.1111/lam.12728 A preliminary study on the relationship between iron and black extrinsic tooth stain in children Zhou J , Zhang Y , Cui P , et al. , 2020 . Gut microbiome changes associated with HIV infection and sexual orientation . Front Cell Infect Microbiol , 10 : 434 . https://doi.org/10.3389/fcimb.2020.00434 https://doi.org/10.3389/fcimb.2020.00434 Gut microbiome changes associated with HIV infection and sexual orientation Lei XU, Zhifang WU, and Shuli DENG contributed to the conception and design of this study. Lei XU, Zhifang WU, Yuan WANG, Sa WANG, Chang SHU, and Zhuhui DUAN performed the experiments. Lei XU, Zhifang WU, and Shuli DENG performed the data analysis. Lei XU wrote the manuscript. Lei XU, Zhifang WU, and Shuli DENG revised the manuscript. All authors have read and approved the final manuscript and, therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. This research was supported by the National Natural Science Foundation of China (No. 81801028) and the Zhejiang Provincial Natural Science Foundation of China (Nos. LQ19H140002 and LGF18H140004). We are grateful to and dearly miss our departed mentor Professor Hui CHEN. 10.1631/jzus.B2000554 1673-1581（2021）04-0285-10 2020-09-14 2020-12-09 2021-03-16 2021-04-15 2022-05-09 © Zhejiang University Press 2021 Lei XU, Zhifang WU, Yuan WANG, Sa WANG, Chang SHU, Zhuhui DUAN, and Shuli DENG declare that they have no conflict of interest. All procedures followed were in accordance with the ethical standards of the ethics committee of the Affiliated Hospital of Stomatology, Zhejiang University School of Medicine and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from parents or guardians of all participants for being included in this study. Compliance with ethics guidelines Data availability 1 The raw sequence reads generated in this study deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (accession Nos. SUB7100449, PRJNA610499, and SRP252283). 浙江大学学报（英文版）（B辑：生物医学和生物技术） 4 22