Metabolomics Insights in Early Childhood Caries

Application of omics technologies in cariology research: A critical review with bibliometric analysis

OBJECTIVES

To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges.

DATA & SOURCES

Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review.

STUDY SELECTION

Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %).

CONCLUSION

This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research.

CLINICAL SIGNIFICANCE

This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.

Manipulating the diseased oral microbiome: the power of probiotics and prebiotics

Dental caries and periodontal disease are amongst the most prevalent global disorders. Their aetiology is rooted in microbial activity within the oral cavity, through the generation of detrimental metabolites and the instigation of potentially adverse host immune responses. Due to the increasing threat of antimicrobial resistance, alternative approaches to readdress the balance are necessary. Advances in sequencing technologies have established relationships between disease and oral dysbiosis, and commercial enterprises seek to identify probiotic and prebiotic formulations to tackle preventable oral disorders through colonisation with, or promotion of, beneficial microbes. It is the metabolic characteristics and immunomodulatory capabilities of resident species which underlie health status. Research emphasis on the metabolic environment of the oral cavity has elucidated relationships between commensal and pathogenic organisms, for example, the sequential metabolism of fermentable carbohydrates deemed central to acid production in cariogenicity. Therefore, a focus on the preservation of an ecological homeostasis in the oral environment may be the most appropriate approach to health conservation. In this review we discuss an ecological approach to the maintenance of a healthy oral environment and debate the potential use of probiotic and prebiotic supplementation, specifically targeted at sustaining oral niches to preserve the delicately balanced microbiome.

Advancing understanding of microbial biofilms through machine learning-powered studies

Microbial biofilms are prevalent in various environments and pose significant challenges to food safety and public health. The biofilms formed by pathogens can cause food spoilage, foodborne illness, and infectious diseases, which are difficult to treat due to their enhanced antimicrobial resistance. While the composition and development of biofilms have been widely studied, their profound impact on food, the food industry, and public health has not been sufficiently recapitulated. This review aims to provide a comprehensive overview of microbial biofilms in the food industry and their implication on public health. It highlights the existence of biofilms along the food-producing chains and the underlying mechanisms of biofilm-associated diseases. Furthermore, this review thoroughly summarizes the enhanced understanding of microbial biofilms achieved through machine learning approaches in biofilm research. By consolidating existing knowledge, this review intends to facilitate developing effective strategies to combat biofilm-associated infections in both the food industry and public health.

The effect of calcium phosphate ion clusters in enhancing enamel conditions versus Duraphat and Icon

This study aimed to evaluate and compare the remineralisation, mechanical, anti-aging, acid resistance and antibacterial properties of calcium phosphate ion clusters (CPICs) materials with those of Duraphat and Icon. The remineralisation and mechanical properties were investigated using scanning electron microscopy, Fourier-transform infrared (FTIR) spectroscopy and nanoindentation. CPICs induced epitaxial crystal growth on the enamel surface, where the regrown enamel-like apatite layers had a similar hardness and elastic modulus to natural enamel (p > 0.05). Acid resistance and anti-aging properties were tested based on ion dissolution and surface roughness. CPICs exhibited similar calcium and phosphate ion dissolution to the control (p > 0.05), and its roughness decreased after thermocycling (p < 0.05), thereby decreasing the risk of enamel surface demineralisation. The minimum inhibitory concentration was 0.1 mg/ml, and the minimum bactericidal concentration ranged from 0.05 to 0.1 mg/ml. Overall, this biomimetic CPICs is a promising alternative to dental demineralisation.

Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries

Streptococcus mutans has been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity of Selenomonas sputigena, Prevotella salivae and Leptotrichia wadei, either individually or with S. mutans. We show that S. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulating S. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability of S. sputigena to colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected with S. mutans, S. sputigena causes extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental disease, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

Salivary microbiome and metabolome analysis of severe early childhood caries

BACKGROUND

Severe early childhood caries (SECC) is an inflammatory disease with complex pathology. Although changes in the oral microbiota and metabolic profile of patients with SECC have been identified, the salivary metabolites and the relationship between oral bacteria and biochemical metabolism remains unclear. We aimed to analyse alterations in the salivary microbiome and metabolome of children with SECC as well as their correlations. Accordingly, we aimed to explore potential salivary biomarkers in order to gain further insight into the pathophysiology of dental caries.

METHODS

We collected 120 saliva samples from 30 children with SECC and 30 children without caries. The microbial community was identified through 16S ribosomal RNA (rRNA) gene high-throughput sequencing. Additionally, we conducted non-targeted metabolomic analysis through ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry to determine the relative metabolite levels and their correlation with the clinical caries status.

RESULTS

There was a significant between-group difference in 8 phyla and 32 genera in the microbiome. Further, metabolomic and enrichment analyses revealed significantly altered 32 salivary metabolites in children with dental caries, which involved pathways such as amino acid metabolism, pyrimidine metabolism, purine metabolism, ATP-binding cassette transporters, and cyclic adenosine monophosphate signalling pathway. Moreover, four in vivo differential metabolites (2-benzylmalate, epinephrine, 2-formaminobenzoylacetate, and 3-Indoleacrylic acid) might be jointly applied as biomarkers (area under the curve = 0.734). Furthermore, the caries status was correlated with microorganisms and metabolites. Additionally, Spearman's correlation analysis of differential microorganisms and metabolites revealed that Veillonella, Staphylococcus, Neisseria, and Porphyromonas were closely associated with differential metabolites.

CONCLUSION

This study identified different microbial communities and metabolic profiles in saliva, which may be closely related to caries status. Our findings could inform future strategies for personalized caries prevention, detection, and treatment.

Understanding the Predictive Potential of the Oral Microbiome in the Development and Progression of Early Childhood Caries

BACKGROUND

Early childhood caries (ECC) is the most common chronic disease in young children and a public health problem worldwide. It is characterized by the presence of atypical and fast progressive caries lesions. The aggressive form of ECC, severe early childhood caries (S-ECC), can lead to the destruction of the whole crown of most of the deciduous teeth and cause pain and sepsis, affecting the child's quality of life. Although the multifactorial etiology of ECC is known, including social, environmental, behavioral, and genetic determinants, there is a consensus that this disease is driven by an imbalance between the oral microbiome and host, or dysbiosis, mediated by high sugar consumption and poor oral hygiene. Knowledge of the microbiome in healthy and caries status is crucial for risk monitoring, prevention, and development of therapies to revert dysbiosis and restore oral health. Molecular biology tools, including next-generation sequencing methods and proteomic approaches, have led to the discovery of new species and microbial biomarkers that could reveal potential risk profiles for the development of ECC and new targets for anti-caries therapies. This narrative review summarized some general aspects of ECC, such as definition, epidemiology, and etiology, the influence of oral microbiota in the development and progression of ECC based on the current evidence from genomics, transcriptomic, proteomic, and metabolomic studies and the effect of antimicrobial intervention on oral microbiota associated with ECC.

CONCLUSION

The evaluation of genetic and proteomic markers represents a promising approach to predict the risk of ECC before its clinical manifestation and plan efficient therapeutic interventions for ECC in its initial stages, avoiding irreversible dental cavitation.

Application of fluoride disturbs plaque microecology and promotes remineralization of enamel initial caries

Background

The caries-preventive effect of topical fluoride application has been corroborated by a number of clinical studies. However, the effect of fluoride on oral microecology remains unclear.

Objective

To monitor the effect of fluoride on dental plaque microecology and demineralization/remineralization balance of enamel initial caries.

Methods

Three-year-old children were enrolled and treated with fluoride at baseline and 6 months. International Caries Detection and Assessment System II indices of 52 subjects were measured at baseline, 3, 6, and 12 months. Supragingival plaque samples of 12 subjects were collected at baseline, 3 and 14 days for 16S rRNA sequencing.

Results

Changes in microbial community structure were observed at 3 days after fluoridation. Significant changes in the relative abundance of microorganisms were observed after fluoride application, especially Capnocytophaga, unidentified Prevotellaceae and Rothia. Functional prediction revealed that cell movement, carbohydrate and energy metabolism were affected significantly after fluoride application. Fluoride significantly inhibited enamel demineralization and promoted remineralization of early demineralized caries enamel at 3 months.

Conclusion

Fluoride application significantly inhibited the progression of enamel initial caries and reversed the demineralization process, possibly by disturbing dental plaque microecology and modulating the physicochemical action of demineralization/remineralization. This deepened our understanding of caries-preventive effects and mechanisms of fluoride.

Fluorescence lectin binding analysis of carbohydrate components in dental biofilms grown in situ in the presence or absence of sucrose

Carbohydrate components, such as glycoconjugates and polysaccharides, are constituents of the dental biofilm matrix that play an important role in biofilm stability and virulence. Exopolysaccharides in Streptococcus mutans biofilms have been characterized extensively, but comparably little is known about the matrix carbohydrates in complex, in situ-grown dental biofilms. The present study employed fluorescence lectin binding analysis (FLBA) to investigate the abundance and spatial distribution of glycoconjugates/polysaccharides in biofilms (n = 306) from 10 participants, grown in situ with (SUC) and without (H2O) exposure to sucrose. Biofilms were stained with 10 fluorescently labeled lectins with different carbohydrate specificities (AAL, ABA, ASA, HPA, LEA, MNA-G, MPA, PSA, VGA and WGA) and analyzed by confocal microscopy and digital image analysis. Microbial composition was determined by 16S rRNA gene sequencing. With the exception of ABA, all lectins targeted considerable matrix biovolumes, ranging from 19.3% to 194.0% of the microbial biovolume in the biofilms, which illustrates a remarkable variety of carbohydrate compounds in in situ-grown dental biofilms. MNA-G, AAL, and ASA, specific for galactose, fucose, and mannose, respectively, stained the largest biovolumes. AAL and ASA biovolumes were increased in SUC biofilms, but the difference was not significant due to considerable biological variation. SUC biofilms were enriched in streptococci and showed reduced abundances of Neisseria and Haemophilus spp., but no significant correlations between lectin-stained biovolumes and bacterial abundance were observed. In conclusion, FLBA demonstrates the presence of a voluminous biofilm matrix comprising a variety of different carbohydrate components in complex, in situ-grown dental biofilms.

Expert consensus on early childhood caries management

Early childhood caries (ECC) is a significant chronic disease of childhood and a rising public health burden worldwide. ECC may cause a higher risk of new caries lesions in both primary and permanent dentition, affecting lifelong oral health. The occurrence of ECC has been closely related to the core microbiome change in the oral cavity, which may be influenced by diet habits, oral health management, fluoride use, and dental manipulations. So, it is essential to improve parental oral health and awareness of health care, to establish a dental home at the early stage of childhood, and make an individualized caries management plan. Dental interventions according to the minimally invasive concept should be carried out to treat dental caries. This expert consensus mainly discusses the etiology of ECC, caries-risk assessment of children, prevention and treatment plan of ECC, aiming to achieve lifelong oral health.

Metabolic differences of the oral microbiome related to dental caries - A pilot study

OBJECTIVE

We aimed to investigate the composition and functions discrepancy of supragingival plaque associated with active deciduous teeth caries in mixed dentitions.

DESIGN

Thirty-three subjects with mixed dentition participated in this study. Children with deciduous teeth caries (dt ≥ 3) were recruited to the caries group, whereas children without deciduous teeth caries (dt = 0) were recruited to the caries-free group. Plaque were collected from deciduous teeth surface and permanent teeth surface respectively. A total of 66 samples of dental plaque were collected and conserved. Illumina 16S rRNA sequencing and diversity analysis were performed for microbiome. Untargeted liquid chromatograph-mass (LC-MS) and partial least squares discriminant analysis were performed for metabolome.

RESULTS

A dominant microbiome of 8 phyla and 22 genera were detected. The alpha diversity indices did not detect differences between the caries and caries-free groups (p > 0.05). Beta diversity analysis showed that the microbiota composition was similar between subgroups. Comparative analysis at genus level did not detect difference between caries and caries-free subgroups. The metabolomics analysis yielded 419 biochemical metabolites, 56 of which were related to caries status. Metabolites in glucose metabolism and byproducts of oxidative stress were identified as related to dental caries in mixed dentition. Dominant bacteria are positively correlated with metabolites, such as Streptococcus and organic acids.

CONCLUSIONS

The upgrade of glucose metabolism and oxidative stress was observed in caries status. Functions discrepancy of oral microbiome may be more pronounced than the composition of oral microbiome with active dental caries in mixed dentitions.

Effect of Dextranase and Dextranase-and-Nisin-Containing Mouthwashes on Oral Microbial Community of Healthy Adults—A Pilot Study

This study analyzed the alteration of oral microbial composition in healthy subjects after using dextranase-containing mouthwash (DMW; Mouthwash formulation I) and dextranase-and-nisin-containing mouthwash (DNMW; Mouthwash formulation II). Eighteen participants were recruited and were randomly allocated to two groups: G1 (DMW user; n = 8) and G2 (DNMW user; n = 10). The subjects were instructed to use the provided mouthwash regularly twice a day for 30 days. The bleeding on probing (BOP), plaque index (PI), probing depth (PBD), and gingival index (GI) were analyzed, and saliva samples were collected before (day 0) and after (day 30) the use of mouthwashes. The saliva metagenomic DNA was extracted and sequenced (next-generation sequencing, Miseq paired-end Illumina 2 × 250 bp platform). The oral microbial community in the pre-and post-treated samples were annotated using QIIME 2™. The results showed the PI and PBD values were significantly reduced in G2 samples. The BOP and GI values of both groups were not significantly altered. The post-treated samples of both groups yielded a reduced amount of microbial DNA. The computed phylogenetic diversity, species richness, and evenness were reduced significantly in the post-treated samples of G2 compared to the post-treated G1 samples. The mouthwash formulations also supported some pathogens’ growth, which indicated that formulations required further improvement. The study needs further experiments to conclude the results. The study suggested that the improved DNMW could be an adjuvant product to improve oral hygiene.

The promise of automated machine learning for the genetic analysis of complex traits

The genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

Science for the Next Century: Deep Phenotyping

Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by the recent availability of high-throughput genomics and multi-omics analyses and the companion tools of advanced informatics and computational science. Understanding of the human genome and its influence on phenotype continues to advance through genotyping large populations and using “light phenotyping” approaches in combination with smaller subsets of the population being evaluated using “deep phenotyping” approaches. Using our capability to integrate and jointly analyze genomic data with other multi-omic data, the knowledge of genotype-phenotype relationships and associated genetic pathways and functions is being advanced. Understanding genotype-phenotype relationships that discriminate human health from disease is speculated to facilitate predictive, precision health care and change modes of health care delivery. The American Association for Dental Research Fall Focused Symposium assembled experts to discuss how studies of genotype-phenotype relationships are illuminating the pathophysiology of craniofacial diseases and developmental biology. Although the breadth of the topic did not allow all areas of dental, oral, and craniofacial research to be addressed (e.g., cancer), the importance and power of integrating genomic, phenomic, and other -omic data are illustrated using a variety of examples. The 8 Fall Focused talks presented different methodological approaches for ascertaining study populations and evaluating population variance and phenotyping approaches. These advances are reviewed in this summary.