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1. Oral Candida Predicts Streptococcus mutans Emergence in Underserved US Infants

   https://doi.org/10.1177/00220345211012385  

   Alkhars, N.; Zeng, Y.; Alomeir, N.; Al Jallad, N.; Wu, T.T.; Aboelmagd, S.; Youssef, M.; Jang, H.; Fogarty, C.; Xiao, J. (January 2021, Journal of Dental Research)

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   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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2. Metagenomic analysis examines oral microbiome changes and interplay with immune response following prenatal total oral rehabilitation

   https://doi.org/10.1186/s12967-023-03997-9  

   Wu, Tong Tong; Sohn, Michael; Manning, Samantha; Beblavy, Robert; Gill, Steven; Quataert, Sally; Vasani, Shruti; Jang, Hoonji; Zeng, Yan; Bruno, Jennifer; et al (December 2023, Journal of Translational Medicine)

   Abstract BackgroundSuboptimal maternal oral health during pregnancy is potentially associated with adverse birth outcomes and increased dental caries risks in children. This study aimed to assess the oral microbiome and immune response following an innovative clinical regimen, Prenatal Total Oral Rehabilitation (PTOR), that fully restores women’s oral health to a “disease-free status” before delivery. MethodsThis prospective cohort study assessed 15 pregnant women at baseline and 3 follow-up visits (1 week, 2 weeks, and 2 months) after receiving PTOR. The salivary and supragingival plaque microbiomes were analyzed using metagenomic sequencing. Multiplexed Luminex cytokine assays were performed to examine immune response following PTOR. The association between salivary immune markers and oral microbiome was further examined. ResultsPTOR was associated with a reduction of periodontal pathogens in plaque, for instance, a lower relative abundance ofTannerella forsythiaandTreponema denticolaat 2 weeks compared to the baseline (p < 0.05). The alpha diversity of plaque microbial community was significantly reduced at the 1-week follow-up (p < 0.05). Furthermore, we observed significant changes in theActinomyces defective-associated carbohydrate degradation pathway andStreptococcus Gordonii-associated fatty acid biosynthesis pathway. Two immune markers related to adverse birth outcomes significantly differed between baseline and follow-up. ITAC, negatively correlated with preeclampsia severity, significantly increased at 1-week follow-up; MCP-1, positively correlated with gestational age, was elevated at 1-week follow-up. Association modeling between immune markers and microbiome further revealed specific oral microorganisms that are potentially correlated with the host immune response. ConclusionsPTOR is associated with alteration of the oral microbiome and immune response among a cohort of underserved US pregnant women. Future randomized clinical trials are warranted to comprehensively assess the impact of PTOR on maternal oral flora, birth outcomes, and their offspring’s oral health. 

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3. Hospital antimicrobial stewardship: profiling the oral microbiome after exposure to COVID-19 and antibiotics

   https://doi.org/10.3389/fmicb.2024.1346762  

   Buendia, Patricia; Fernandez, Krystal; Raley, Castle; Rahnavard, Ali; Crandall, Keith A; Castro, Jose Guillermo (February 2024, Frontiers in Microbiology)

   IntroductionDuring the COVID-19 Delta variant surge, the CLAIRE cross-sectional study sampled saliva from 120 hospitalized patients, 116 of whom had a positive COVID-19 PCR test. Patients received antibiotics upon admission due to possible secondary bacterial infections, with patients at risk of sepsis receiving broad-spectrum antibiotics (BSA). MethodsThe saliva samples were analyzed with shotgun DNA metagenomics and respiratory RNA virome sequencing. Medical records for the period of hospitalization were obtained for all patients. Once hospitalization outcomes were known, patients were classified based on their COVID-19 disease severity and the antibiotics they received. ResultsOur study reveals that BSA regimens differentially impacted the human salivary microbiome and disease progression. 12 patients died and all of them received BSA. Significant associations were found between the composition of the COVID-19 saliva microbiome and BSA use, between SARS-CoV-2 genome coverage and severity of disease. We also found significant associations between the non-bacterial microbiome and severity of disease, withCandida albicansdetected most frequently in critical patients. For patients who did not receive BSA before saliva sampling, our study suggestsStaphylococcus aureusas a potential risk factor for sepsis. DiscussionOur results indicate that the course of the infection may be explained by both monitoring antibiotic treatment and profiling a patient’s salivary microbiome, establishing a compelling link between microbiome and the specific antibiotic type and timing of treatment. This approach can aid with emergency room triage and inpatient management but also requires a better understanding of and access to narrow-spectrum agents that target pathogenic bacteria. 

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4. Modeling Oral Multispecies Biofilm Recovery After Antibacterial Treatment

   https://doi.org/10.1038/s41598-018-37170-w  

   Jing, Xiaobo; Huang, Xiangya; Haapasalo, Markus; Shen, Ya; Wang, Qi (January 2019, Scientific Reports)

   Abstract Recovery of multispecies oral biofilms is investigated following treatment by chlorhexidine gluconate (CHX), iodine-potassium iodide (IPI) and Sodium hypochlorite (NaOCl) both experimentally and theoretically. Experimentally, biofilms taken from two donors were exposed to the three antibacterial solutions (irrigants), respectively, for 10 minutes. We observe that (a) live bacterial cell ratios decline for a week after the exposure and the trend then reverses beyond the week; after fifteen weeks, live bacterial cell ratios in biofilms fully return to their pretreatment levels; (b) NaOCl is shown as the strongest antibacterial agent for the oral biofilms; (c) multispecies oral biofilms from different donors showed no difference in their susceptibility to all the bacterial solutions. Guided by the experiment, a mathematical model for biofilm dynamics is developed, accounting for multiple bacterial phenotypes, quorum sensing, and growth factor proteins, to describe the nonlinear time evolutionary behavior of the biofilms. The model captures time evolutionary dynamics of biofilms before and after antibacterial treatment very well. It reveals the important role played by quorum sensing molecules and growth factors in biofilm recovery and verifies that the source of biofilms has a minimal effect to their recovery. The model is also applied to describe the state of biofilms of various ages treated respectively by CHX, IPI and NaOCl, taken from different donors. Good agreement with experimental data predicted by the model is obtained as well, confirming its applicability to modeling biofilm dynamics in general. 

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5. Machine Learning Approach Identified Multi-Platform Factors for Caries Prediction in Child-Mother Dyads

   https://doi.org/10.3389/fcimb.2021.727630  

   Wu, Tong Tong; Xiao, Jin; Sohn, Michael B.; Fiscella, Kevin A.; Gilbert, Christie; Grier, Alex; Gill, Ann L.; Gill, Steve R. (August 2021, Frontiers in Cellular and Infection Microbiology)

   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. 

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