Early childhood caries (ECC) is not only the most common chronic childhood disease but also disproportionately affects underserved populations. Of those, children living in Thailand have been found to have high rates of ECC and severe ECC. Frequently, the cause of ECC is blamed on a handful of cariogenic organisms, such as Streptococcus mutans and Streptococcus sobrinus . However, ECC is a multifactorial disease that results from an ecological shift in the oral cavity from a neutral pH (~7.5) to an acidic pH (<5.5) environment influenced by the host individual’s biological, socio-behavioral, and lifestyle factors. Currently, there is a lack of understanding of how risk factors at various levels influence the oral health of children at risk. We applied a statistical machine learning approach for multimodal data integration (parallel and hierarchical) to identify caries-related multiplatform factors in a large cohort of mother-child dyads living in Chiang Mai, Thailand (N=177). Whole saliva (1 mL) was collected from each individual for DNA extraction and 16S rRNA sequencing. A set of maternal and early childhood factors were included in the data analysis. Significantly, vaginal delivery, preterm birth, and frequent sugary snacking were found to increase the risk for ECC. The salivary microbial diversity was significantly different in children with ECC or without ECC. Results of linear discriminant analysis effect size (LEfSe) analysis of the microbial community demonstrated that S. mutans , Prevotella histicola , and Leptotrichia hongkongensis were significantly enriched in ECC children. Whereas Fusobacterium periodonticum was less abundant among caries-free children, suggesting its potential to be a candidate biomarker for good oral health. Based on the multimodal data integration and statistical machine learning models, the study revealed that the mode of delivery and snack consumption outrank salivary microbiome in predicting ECC in Thai children. The biological and behavioral factors may play significant roles in the microbial pathobiology of ECC and warrant further investigation.
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Oral Candida Predicts Streptococcus mutans Emergence in Underserved US Infants
Despite the cariogenic role of Candida suggested from recent studies, oral Candida acquisition in children at high risk for early childhood caries (ECC) and its association with cariogenic bacteria Streptococcus mutans remain unclear. Although ECC disproportionately afflicts socioeconomically disadvantaged and racial-minority children, microbiological studies focusing on the underserved group are scarce. Our prospective cohort study examined the oral colonization of Candida and S. mutans among 101 infants exclusively from a low-income and racial-minority background in the first year of life. The Cox hazard proportional model was fitted to assess factors associated with the time to event of the emergence of oral Candida and S. mutans. Oral Candida colonization started as early as 1 wk among 13% of infants, increased to 40% by 2 mo, escalated to 48% by 6 mo, and remained the same level until 12 mo. S. mutans in saliva was detected among 20% infants by 12 mo. The emergence of S. mutans by year 1 was 3.5 times higher (hazard ratio [HR], 3.5; confidence interval [CI], 1.1–11.3) in infants who had early colonization of oral Candida compared to those who were free of oral Candida ( P = 0.04) and 3 times higher (HR, 3.0; CI, 1.3–6.9) among infants whose mother had more than 3 decayed teeth ( P = 0.01), even after adjusting demographics, feeding, mother’s education, and employment status. Infants’ salivary S. mutans abundance was positively correlated with infants’ Candida albicans ( P < 0.01) and Candida krusei levels ( P < 0.05). Infants’ oral colonization of C. albicans was positively associated with mother’s oral C. albicans carriage and education ( P < 0.01) but negatively associated with mother’s employment status ( P = 0.01). Future studies are warranted to examine whether oral Candida modulates the oral bacterial community as a whole to become cariogenic during the onset and progression of ECC, which could lead to developing novel ECC predictive and preventive strategies from a fungal perspective.
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- Award ID(s):
- 1934962
- NSF-PAR ID:
- 10288784
- Date Published:
- Journal Name:
- Journal of Dental Research
- ISSN:
- 0022-0345
- Page Range / eLocation ID:
- 002203452110123
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Purpose/aim To analyze potential factors associated with levels of selected oral pathogens, as well as total aerobic bacterial species, among nursing home residents.
Materials and methods Nursing home residents were divided into three groups (G1 included people with teeth but no dentures, G2 included people with teeth and dentures, and G3 included people with no teeth and with dentures). All participants had microbiological samples collected from their oral cavity and dentures. Counts of total aerobic bacterial species,
Porphyromonas gingivalis ,Fusobacterium nucleatum ,Actinomyces viscosus ,Aggregatibacter actinomycetemcomitans , andCandida albicans were compared among groups using the Wilcoxon rank sum test. A multivariate analysis was also performed to control other available covariates.Results Bivariate analysis revealed significant differences among the groups, and multivariate analysis showed that sex, the presence of natural teeth, denture wearing, oral hygiene indices, and systemic health conditions were associated with bacterial and
Candida albicans log counts.Conclusions Presence of natural teeth and denture wearing, as well as oral hygiene, sex and systemic health conditions were associated with bacterial and
Candida albicans log counts among nursing home residents. -
Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated ( Prevotella histicola, Streptococcus mutans , and Rothia muciloginosa ) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.more » « less
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This study aimed to evaluate the impact of Nystatin oral rinse on salivary and supragingival microbiota in adults with oral candidiasis and identify predictive factors related to individuals’ responses to Nystatin. The trial involved twenty participants who used 600,000 International Units/application of Nystatin oral rinse for seven days, four times a day, and were followed up at one week and three months after the rinse. The salivary and plaque microbiome of the participants were assessed via 16S rDNA amplicon sequencing. Overall, salivary and plaque microbiomes remained stable. However, among the participants (53 percent) who responded to Nystatin rinse (defined as free of oral Candida albicans post treatment), Veillonella emerged as a core genus alongside Streptococcus and Actinomyces in supragingival plaque at the 3-month follow-up. Furthermore, statistical models were fit to identify predictive factors of Nystatin rinse success (elimination of C. albicans) or failure (remaining C. albicans). The results revealed that an increased level of salivary Interferon (IFN)-γ-inducible protein (IP-10), also known as C-X-C motif chemokine ligand 10 (CXCL10), was an indicator of a failure of responding to Nystatin rinse. Future clinical trials are warranted to comprehensively assess the impact of antifungal treatment on the oral flora.
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Abstract Histatin-5 (Hist-5) is an antimicrobial peptide found in human saliva that functions to defend the oral cavity from microbial infections, such as those caused by the fungal pathogen Candida albicans (C. albicans). Hist-5 can bind Cu in multiple oxidation states, Cu2+ and Cu+ in vitro, and supplemental Cu2+ has been shown to improve the fungicidal activity of the peptide against C. albicans in culture. However, the exact role of Cu on the antifungal activity of Hist-5 and whether direct peptide–Cu interactions occur intracellularly has yet to be fully determined. Here, we used a combination of fluorescence spectroscopy and confocal microscopy experiments to show reversible Cu-dependent quenching of a fluorescent Hist-5 analogue, Hist-5*, indicating a direct interaction between Hist-5 and intracellular Cu. X-ray fluorescence microscopy images revealed peptide-induced changes to cellular Cu distribution and cell-associated Cu content. These data support a model in which Hist-5 can facilitate the hyperaccumulation of Cu in C. albicans and directly interact with Cu intracellularly to increase the fungicidal activity of Hist-5.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/00220345211012385
