Oral microbiome characteristics in children with and without early childhood caries

Objective: Early childhood caries (ECC) negatively affects children’s growth due to its close relation to an imbalance of the oral microbiota. This study aimed to evaluate the distribution of the oral microbiota in children with ECC and healthy individuals. Methods: The oral microbiota of 20 children with dental caries from both carious teeth (CC cohort) and healthy teeth (CH cohort), and the oral microbiota of 20 healthy control children (HH cohort) were subjected to 16S rDNA sequencing. Results: The results revealed significant differences between the microbial structure of the CC and CH cohorts of every child with ECC. The most common microbes were Streptococcus, Neisseria, Leptotrichia, Lautropia and Haemophilus. Specifically, the CC cohort contained Lactobacillus, Veillonella, and Prevotella 7, the CH cohort contained Actinomyces, Bifidobacterium and Abiotrophia, and the HH cohort mainly contained Neisseria, Leptotrichia, Porphyromonas and Gemella. Lastly, we established a random forest model consisting of 10 genera (Prevotella 7, Actinobacillus, etc.) which demonstrated promising clinical diagnostic ability (area under the curve (AUC) = 89.8%). These findings indicate that oral microbiota can potentially be used as therapeutic targets or diagnostic markers for the early prediction and prevention of caries in children.

Early childhood caries; Oral microbiota; 16S rDNA sequencing; Randomforest model; Biomarker

[1] Lin T, Lin C, Pan T. The implication of probiotics in the prevention of dental caries. Applied Microbiology and Biotechnology. 2018; 102: 577–586.

[2] Organization WH. World Health Assembly Resolution paves the way for better oral health care. 2021. Available at: https://www. who.int/news/item/27-05-2021-world-health-assembly-resolution-paves-the-way-for-better-oral-health-care (Accessed: 27 May 2021).

[3] Tinanoff N, Baez RJ, Diaz Guillory C, Donly KJ, Feldens CA, McGrath C, et al. Early childhood caries epidemiology, aetiology, risk assessment, societal burden, management, education, and policy: global perspective. International Journal of Paediatric Dentistry. 2019; 29: 238–248.

[4] Pitts NB, Zero DT, Marsh PD, Ekstrand K, Weintraub JA, Ramos-Gomez F, et al. Dental caries. Nature Reviews Disease Primers. 2017; 3: 17030.

[5] Xiao J, Fiscella KA, Gill SR. Oral microbiome: possible harbinger for children’s health. International Journal of Oral Science. 2020; 12: 12.

[6] Li X, Zheng J, Ma X, Zhang B, Zhang J, Wang W, et al. The oral microbiome of pregnant women facilitates gestational diabetes discrimination. Journal of Genetics and Genomics. 2021; 48: 32–39.

[7] Wang T, Yu L, Xu C, Pan K, Mo M, Duan M, et al. Chronic fatigue syndrome patients have alterations in their oral microbiome composition and function. PLoS One. 2018; 13: e0203503.

[8] Wang J, Jia Z, Zhang B, Peng L, Zhao F. Tracing the accumulation of in vivo human oral microbiota elucidates microbial community dynamics at the gateway to the GI tract. Gut. 2020; 69: 1355–1356.

[9] Huang S, Li R, Zeng X, He T, Zhao H, Chang A, et al. Predictive modeling of gingivitis severity and susceptibility via oral microbiota. The ISME Journal. 2014; 8: 1768–1780.

[10] Wang Y, Wang S, Wu C, Chen X, Duan Z, Xu Q, et al. Oral microbiome alterations associated with early childhood caries highlight the importance of carbohydrate metabolic activities. MSystems. 2019; 4: e00450–19.

[11] Dashper SG, Mitchell HL, Lê Cao KA, Carpenter L, Gussy MG, Calache H, et al. Temporal development of the oral microbiome and prediction of early childhood caries. Scientific Reports. 2019; 9: 19732.

[12] Teng F, Yang F, Huang S, Bo C, Xu Z, Amir A, et al. Prediction of early childhood caries via spatial-temporal variations of oral microbiota. Cell Host & Microbe. 2015; 18: 296–306.

[13] Dikmen B. Icdas II criteria (international caries detection and assessment system). Journal of Istanbul University Faculty of Dentistry. 2015; 49: 63–72.

[14] Ling Z, Kong J, Jia P, Wei C, Wang Y, Pan Z, et al. Analysis of oral microbiota in children with dental caries by PCR-DGGE and barcoded pyrosequencing. Microbial Ecology. 2010; 60: 677–690.

[15] Wang L, Pan M, Li D, Yin Y, Jiang T, Fang S, et al. Metagenomic insights into the effects of oligosaccharides on the microbial composition of cecal contents in constipated mice. Journal of Functional Foods. 2017; 38: 486–496.

[16] Wang L, Hu L, Xu Q, Yin B, Fang D, Wang G, et al. Bifidobacterium adolescentis exerts strain-specific effects on constipation induced by loperamide in BALB/c mice. International Journal of Molecular Sciences. 2017; 18: 318.

[17] Han M, Yang K, Yang P, Zhong C, Chen C, Wang S, et al. Stratification of athletes’ gut microbiota: the multifaceted hubs associated with dietary factors, physical characteristics and performance. Gut Microbes. 2020; 12: 1842991.

[18] Paul D, Kumbhare SV, Mhatre SS, Chowdhury SP, Shetty SA, Marathe NP, et al. Exploration of microbial diversity and community structure of lonar lake: the only hypersaline meteorite crater lake within basalt rock. Frontiers in Microbiology. 2015; 6: 1553.

[19] Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology. 2005; 71: 8228–8235.

[20] Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biology. 2011; 12: R60.

[21] Howsmon DP, Hahn J. Regularization techniques to overcome overparameterization of complex biochemical reaction networks. IEEE Life Sciences Letters. 2016; 2: 31–34.

[22] Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest:  a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences. 2003; 43: 1947–1958.

[23] McGraw R, Zhang R. Multivariate analysis of homogeneous nucleation rate measurements. Nucleation in the p-toluic acid/sulfuric acid/water system. The Journal of Chemical Physics. 2008; 128: 064508.

[24] Seow WK. Early childhood caries. Pediatric Clinics of North America. 2018; 65: 941–954.

[25] Xia Y, Sun J, Chen D. Community diversity measures and calculations. Statistical Analysis of Microbiome Data with R. 2018; 14: 167–190.

[26] Chen W, Jiang Q, Yan G, Yang D. The oral microbiome and salivary proteins influence caries in children aged 6 to 8 years. BMC Oral Health. 2020; 20: 295.

[27] Xiao C, Ran S, Huang Z, Liang J. Bacterial diversity and community structure of supragingival plaques in adults with dental health or caries revealed by 16S pyrosequencing. Frontiers in Microbiology. 2016; 7: 1145.

[28] Whittaker RH. Vegetation of the siskiyou mountains, oregon and california. Ecological Monographs. 1960; 30: 279–338.

[29] Costalonga M, Herzberg MC. The oral microbiome and the immunobiology of periodontal disease and caries. Immunology Letters. 2014; 162: 22–38.

[30] Luo YX, Sun ML, Shi PL, Liu P, Chen YY, Peng X. Research progress in the relationship between Veillonella and oral diseases. Hua Xi Kou Qiang Yi Xue Za Zhi. 2020; 38: 576–582. (In Chinese)

[31] Kanasi E, Dewhirst FE, Chalmers NI, Kent, Jr. R, Moore A, Hughes CV, et al. Clonal analysis of the microbiota of severe early childhood caries. Caries Research. 2010; 44: 485–497.

[32] Yamashita Y, Takeshita T. The oral microbiome and human health. Journal of Oral Science. 2017; 59: 201–206.

[33] Fakhruddin KS, Ngo HC, Samaranayake LP. Cariogenic microbiome and microbiota of the early primary dentition: a contemporary overview. Oral Diseases. 2019; 25: 982–995.

[34] Blanchet L, Vitale R, van Vorstenbosch R, Stavropoulos G, Pender J, Jonkers D, et al. Constructing bi-plots for random forest: Tutorial. Analytica Chimica Acta. 2020; 1131: 146–155.

[35] Muschelli J. ROC and AUC with a binary predictor: a potentially misleading metric. Journal of Classification. 2020; 37: 696–708.