Abstract:pellicle is the initial proteinaceous layer that is formed almost instantaneously on all solid surfaces in the oral cavity. it is of essential relevance for any interactions and metabolism on the tooth surface. Up to now, there is no information on the metabolome of this structure. Accordingly, the present study aims to characterise the metabolomic profile of in-situ pellicle in children with different caries activity for the first time in comparison to saliva. Small molecules such as carbohydrates, amino acid… Show more
“…There is unavoidable and unobserved complexity in what is sampled and measured in observational supragingival biofilm metabolomics studies like the present one. For example, the origins of the metabolome: Schulz et al (2020) recently conducted targeted metabolomics analyses of initially formed (10 min) pellicle in young children and found that the identified metabolites were not significantly different from what was quantified in saliva. Importantly, the most abundant metabolites, including acetic acid, propionic acid, glycine, serine, galactose and mannose, lactose, glucose, palmitic acid, and stearic acid, were identified in the virtual absence of bacterial colonization.…”
Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose ( P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin ( P = 4.7 × 10−6) and epicatechin ( P = 2.9 × 10−6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.
“…There is unavoidable and unobserved complexity in what is sampled and measured in observational supragingival biofilm metabolomics studies like the present one. For example, the origins of the metabolome: Schulz et al (2020) recently conducted targeted metabolomics analyses of initially formed (10 min) pellicle in young children and found that the identified metabolites were not significantly different from what was quantified in saliva. Importantly, the most abundant metabolites, including acetic acid, propionic acid, glycine, serine, galactose and mannose, lactose, glucose, palmitic acid, and stearic acid, were identified in the virtual absence of bacterial colonization.…”
Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose ( P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin ( P = 4.7 × 10−6) and epicatechin ( P = 2.9 × 10−6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.
“…This leads to early phases of biofilm formation and by the interconnection with other species to the establishment of mature plaque (see next chapter). However, it is important to note that according to recent studies, the ultrastructure, enzyme activities, and proteomic profile of the acquired pellicle do not show major differences between otherwise healthy caries-active and inactive subjects [ 67 , 72 , 73 ]. Fig.…”
Background
All soft and solid surface structures in the oral cavity are covered by the acquired pellicle followed by bacterial colonization. This applies for natural structures as well as for restorative or prosthetic materials; the adherent bacterial biofilm is associated among others with the development of caries, periodontal diseases, peri-implantitis, or denture-associated stomatitis. Accordingly, there is a considerable demand for novel materials and coatings that limit and modulate bacterial attachment and/or propagation of microorganisms.
Objectives and findings
The present paper depicts the current knowledge on the impact of different physicochemical surface characteristics on bioadsorption in the oral cavity. Furthermore, it was carved out which strategies were developed in dental research and general surface science to inhibit bacterial colonization and to delay biofilm formation by low-fouling or “easy-to-clean” surfaces. These include the modulation of physicochemical properties such as periodic topographies, roughness, surface free energy, or hardness. In recent years, a large emphasis was laid on micro- and nanostructured surfaces and on liquid repellent superhydrophic as well as superhydrophilic interfaces. Materials incorporating mobile or bound nanoparticles promoting bacteriostatic or bacteriotoxic properties were also used. Recently, chemically textured interfaces gained increasing interest and could represent promising solutions for innovative antibioadhesion interfaces. Due to the unique conditions in the oral cavity, mainly in vivo or in situ studies were considered in the review.
Conclusion
Despite many promising approaches for modulation of biofilm formation in the oral cavity, the ubiquitous phenomenon of bioadsorption and adhesion pellicle formation in the challenging oral milieu masks surface properties and therewith hampers low-fouling strategies.
Clinical relevance
Improved dental materials and surface coatings with easy-to-clean properties have the potential to improve oral health, but extensive and systematic research is required in this field to develop biocompatible and effective substances.
“…Furthermore, it is necessary to normalize the data obtained in each group to avoid errors associated with the raw data [126,128]. Traditionally, many researchers use the p-value as the main statistical tool in order to identify analytes whose variation is statistically significant [129][130][131][132]. However, this approach is extremely limited and often results in numerous false positives and negatives [133,134].…”
Section: Discussionmentioning
confidence: 99%
“…Principal component analysis (PCA) is the most common method used in unsupervised analysis [49,128,130,131,137]. This approach reduces the number of associated factors by showing clusters according to the separation between groups.…”
Section: Discussionmentioning
confidence: 99%
“…There are several LDA methods for the analysis of biological compounds, such as the discriminant analysis of partial least squares (PLS-DA) [35,49,137]. Due to its ability to distinguish between groups, identifying which compounds are responsible for each separation, PLS-DA is an extremely useful tool in understanding the metabolites that are responsible for the separation between the control and the patients groups under study [35,49,126,128,131,[136][137][138][139]. However, it is important to note that for the success of this method, the PLS-DA must be optimized a priori [140], with its linear model being trained so that its precision is high and the errors evaluated [126,128,139].…”
Fostered by the advances in the instrumental and analytical fields, in recent years the analysis of volatile organic compounds (VOCs) has emerged as a new frontier in medical diagnostics. VOCs analysis is a non-invasive, rapid and inexpensive strategy with promising potential in clinical diagnostic procedures. Since cellular metabolism is altered by diseases, the resulting metabolic effects on VOCs may serve as biomarkers for any given pathophysiologic condition. Human VOCs are released from biomatrices such as saliva, urine, skin emanations and exhaled breath and are derived from many metabolic pathways. In this review, the potential of VOCs present in saliva will be explored as a monitoring tool for several oral diseases, including gingivitis and periodontal disease, dental caries, and oral cancer. Moreover, the analytical state-of-the-art for salivary volatomics, e.g., the most common extraction techniques along with the current challenges and future perspectives will be addressed unequivocally.
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