Oral microbiome characteristics in children with and without early childhood caries
1Department of Stomatology, Wuxi Children’s Hospital, 214023 Wuxi, Jiangsu, China
2State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China
3School of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China
4Wuxi Translational Medicine Research Center and Jiangsu Translational Medicine Research Institute Wuxi Branch, 214122 Wuxi, Jiangsu, China
5National Engineering Research Center for Functional Food, Jiangnan University, 214122 Wuxi, Jiangsu, China
DOI: 10.22514/jocpd.2023.012 Vol.47,Issue 2,March 2023 pp.58-67
Submitted: 14 October 2022 Accepted: 05 December 2022
Published: 03 March 2023
*Corresponding Author(s): Qiuxiang Zhang E-mail: firstname.lastname@example.org
† These authors contributed equally.
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
Xianyin Xu,Baokun Shan,Qiuxiang Zhang,Wenwei Lu,Jianxin Zhao,Hao Zhang,Wei Chen. Oral microbiome characteristics in children with and without early childhood caries. Journal of Clinical Pediatric Dentistry. 2023. 47(2);58-67.
 Lin T, Lin C, Pan T. The implication of probiotics in the prevention of dental caries. Applied Microbiology and Biotechnology. 2018; 102: 577–586.
 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).
 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.
 Pitts NB, Zero DT, Marsh PD, Ekstrand K, Weintraub JA, Ramos-Gomez F, et al. Dental caries. Nature Reviews Disease Primers. 2017; 3: 17030.
 Xiao J, Fiscella KA, Gill SR. Oral microbiome: possible harbinger for children’s health. International Journal of Oral Science. 2020; 12: 12.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 Dikmen B. Icdas II criteria (international caries detection and assessment system). Journal of Istanbul University Faculty of Dentistry. 2015; 49: 63–72.
 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.
 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.
 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.
 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.
 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.
 Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology. 2005; 71: 8228–8235.
 Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biology. 2011; 12: R60.
 Howsmon DP, Hahn J. Regularization techniques to overcome overparameterization of complex biochemical reaction networks. IEEE Life Sciences Letters. 2016; 2: 31–34.
 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.
 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.
 Seow WK. Early childhood caries. Pediatric Clinics of North America. 2018; 65: 941–954.
 Xia Y, Sun J, Chen D. Community diversity measures and calculations. Statistical Analysis of Microbiome Data with R. 2018; 14: 167–190.
 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.
 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.
 Whittaker RH. Vegetation of the siskiyou mountains, oregon and california. Ecological Monographs. 1960; 30: 279–338.
 Costalonga M, Herzberg MC. The oral microbiome and the immunobiology of periodontal disease and caries. Immunology Letters. 2014; 162: 22–38.
 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)
 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.
 Yamashita Y, Takeshita T. The oral microbiome and human health. Journal of Oral Science. 2017; 59: 201–206.
 Fakhruddin KS, Ngo HC, Samaranayake LP. Cariogenic microbiome and microbiota of the early primary dentition: a contemporary overview. Oral Diseases. 2019; 25: 982–995.
 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.
 Muschelli J. ROC and AUC with a binary predictor: a potentially misleading metric. Journal of Classification. 2020; 37: 696–708.
Vol., Issue , Invalid dateTable of contents
Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.
PubMed (MEDLINE) PubMed comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.
Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.
Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.
JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.
Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.
BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.
Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.