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Species-Level Imaging Reveals Habitat Tropisms (A-E) Nested probe sets show cells hybridizing to both species-specific and genus-level probes. (A) All Rothia cells (Rot) imaged were identified as R. mucilaginosa (R.muc). (B and C) Many Actinomyces cells (Act) were identified as members of the A. odontolyticus group (A.odo) (B), whereas some were A. graevenitzii (A.gra) (C). (D and E) Most Neisseriaceae (Nei) were identified as N. flavescens (N.fla) (D). A probe (N.sub/N.fla) that detected both N. subflava and N. flavescens also identified most cells (E). (F) The Streptococcus mitis group (S.mit) occurred as a thin layer at the exterior of the structure and in stripes between domains of other taxa. In contrast, S. salivarius and S. vestibularis (S.sal) occurred as large clusters of cells within the consortium. See also Figures S4 and S6, Video S2, and Tables S2 and S3.
Source publication
A fundamental question in microbial ecology is how microbes are spatially organized with respect to each other and their host. A test bed for examining this question is the tongue dorsum, which harbors a complex and important microbial community. Here, we use multiplexed fluorescence spectral imaging to investigate the organization of the tongue mi...
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... for other abundant genera were also included in each set so as to visualize the target in context (see Table S3 for the full probe set composition). Prevalence within all donors and the frequency of identification across all images are reported in Table S4, and representative images are shown in Figure 4. ...
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... results were consistent with the predictions from sequence data. All visualized Rothia cells were identified as R. mucilaginosa ( Figure 4A; Table S4), which is in agreement with the expectation based on HMP sequencing results ( Figure 1C). In the genus Actinomyces, the probe targeting Actinomyces odontolyticus and its close relatives generally colocalized with the genus probe ( Figure 4B), whereas the probe targeting Actinomyces graevenitzii highlighted a small fraction of the cells in the genus. ...
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... visualized Rothia cells were identified as R. mucilaginosa ( Figure 4A; Table S4), which is in agreement with the expectation based on HMP sequencing results ( Figure 1C). In the genus Actinomyces, the probe targeting Actinomyces odontolyticus and its close relatives generally colocalized with the genus probe ( Figure 4B), whereas the probe targeting Actinomyces graevenitzii highlighted a small fraction of the cells in the genus. Therefore, the A. odontolyticus species group appeared to be the dominant member and A. graevenitzii a lesser member of the genus on the tongue, which is, again, consistent with sequence data. ...
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... Nfla469 probe was designed to hybridize only with N. flavescens and related species (N. flavescens group; Table S1), whereas the Nsubfla177 probe was designed to hybridize with both Neisseria subflava and N. flavescens ( Figure S4). Both probes showed near-complete co-localization with the Neisseriaceae family probe ( Figures 4D and 4E), suggesting that N. flavescens is dominant in these TD consortia as expected and that N. subflava is a minor player if present (Table S4). ...
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... group; Table S1), whereas the Nsubfla177 probe was designed to hybridize with both Neisseria subflava and N. flavescens ( Figure S4). Both probes showed near-complete co-localization with the Neisseriaceae family probe ( Figures 4D and 4E), suggesting that N. flavescens is dominant in these TD consortia as expected and that N. subflava is a minor player if present (Table S4). ...
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... the genus Streptococcus, imaging revealed differential patterns of spatial localization ( Figure 4F). We used three subgenus-level probes: one targeting S. mitis and its close relatives Streptococcus oralis and Streptococcus infantis (S. mitis group in Table S1; hereafter referred to collectively as S. mitis); one targeting Streptococcus salivarius and the related Streptococcus vestibularis, hereafter referred to collectively as S. salivarius; and one targeting Streptococcus parasanguinis, including both biovars recognized in HOMD. ...
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... indicates that both species play an important role in TD consortia. However, S. mitis was frequently located on the surface of structures as well as forming internal stripes, whereas S. salivarius occupied large patches with distinctive cellular morphology ( Figure 4F). The third major Streptococcus on the tongue, S. parasanguinis, was detected primarily in stripes ( Figure S6). ...
Citations
... Alternatively, N. mucosa, N. elongata, and K. oralis could each be specialized for a distinctive microhabitat within plaque, and the abundance of each taxon in a given plaque sample could be a consequence of patchy distribution of these microhabitats. Other oral bacteria are known to specialize for microhabitats within plaque (61), and oral biofilms are inhomogeneous, with patches dominated by one or a few taxa (62,63). Further research is needed to disentangle these factors and better understand the drivers of microbial dominance in this habitat. ...
The human oral microbiome is a diverse ecosystem in which bacterial species have evolved to occupy specific niches within the oral cavity. The Neisseriaceae family, which includes human oral species in the genera Neisseria, Eikenella, Kingella, and Simonsiella, plays a significant role in both commensal and pathogenic relationships. In this study, we investigate the distribution and functional adaptations of Neisseriaceae species across oral habitats, focusing on their site tropisms and ecological roles. We employed a metapangenomic approach in which a curated set of reference genomes representing Neisseriaceae diversity was used for competitive mapping of metagenomic reads. Our analysis revealed distinct habitat preferences among Neisseriaceae species, with Kingella oralis, Neisseria elongata, and Neisseria mucosa primarily found in dental plaque; Neisseria subflava on the tongue dorsum; and Neisseria cinerea in the keratinized gingiva. Functional enrichment analyses identified genes and pathways underpinning habitat-specific adaptations. Plaque specialists showed metabolic versatility, with adaptations in nitrogen metabolism, including nitrate reduction and denitrification, lysine degradation, and galactose metabolism. Tongue dorsum specialists exhibited adaptations including enhanced capabilities for amino acid biosynthesis, short-chain fatty acid and glycerol transport, as well as lipopolysaccharide glycosylation, which may aid in resisting antimicrobial peptides and maintaining membrane integrity. These findings provide insights into the ecological roles and adaptive strategies of Neisseriaceae species within the human oral microbiome and establish a foundation for exploring functional specialization and microbial interactions in these niches.
IMPORTANCE
Unraveling the distribution and functional adaptations of Neisseriaceae within the human oral microbiome is essential for understanding the roles of these abundant and prevalent commensals in both health and disease. Through a metapangenomic approach, we uncovered distinct habitat preferences of various Neisseriaceae taxa across the oral cavity and identified key genetic traits that may drive their habitat specialization and role in host-microbe interactions. These insights enhance our understanding of the microbial dynamics that shape oral microbial ecology, offering potential pathways for advancing oral health research.
... Detailed data collection methods for microbiome biofilm image data were described in Wilbert et al. (2020). Briefly, the microbial biofilm from the human tongue dorsum was collected by scraping a ridged plastic tongue scraper over the tongue from back to front. ...
... To identify the distribution of major microbes, multiplexed, spectral imaging fluorescence in situ hybridization (FISH) was performed targeting the 17 abundant genera as well as 7 abundant species within these genera, plus 1 phylum. A total of 100 original images (20 images per subject across 5 subjects) were generated, and the following taxa presented in all subjects and in ≥ 69% of images acquired (supplemental Table 4 in Wilbert et al., 2020): Rothia, Actinomyces, Streptococcus, Veillonella, and Neisseriaceae (genus Actinomyces has recently been split into two genera, Actinomyces and ...
... We applied the proposed BayesFlow framework to two distinct microbial biofilm images from Wilbert et al. (2020). Specifically, we trained the neural network on simulated data sets, focusing on the well-defined perimeter of the consortia while excluding inner host area given each microbiome biofilm image, and leveraged 20 the framework to perform the inference of spatial self-association for multiple taxa. ...
It is common in nature to see aggregation of objects in space. Exploring the mechanism associated with the locations of such clustered observations can be essential to understanding the phenomenon, such as the source of spatial heterogeneity, or comparison to other event generating processes in the same domain. Log-Gaussian Cox processes (LGCPs) represent an important class of models for quantifying aggregation in a spatial point pattern. However, implementing likelihood-based Bayesian inference for such models presents many computational challenges, particularly in high dimensions. In this paper, we propose a novel likelihood-free inference approach for LGCPs using the recently developed BayesFlow approach, where invertible neural networks are employed to approximate the posterior distribution of the parameters of interest. BayesFlow is a neural simulation-based method based on "amortized" posterior estimation. That is, after an initial training procedure, fast feed-forward operations allow rapid posterior inference for any data within the same model family. Comprehensive numerical studies validate the reliability of the framework and show that BayesFlow achieves substantial computational gain in repeated application, especially for two-dimensional LGCPs. We demonstrate the utility and robustness of the method by applying it to two distinct oral microbial biofilm images.
... From one slide for each subject, 20 large consortia were selected for further analysis. Detailed data collection methods have been described previously (Wilbert et al., 2020). The FISH probes employed in this study targeted 1 phylum, 17 genera, and 7 species within these genera. ...
... The magnitude of these estimated spatial inter-taxon correlations ( Figure 5) aligned with that reported in previous studies (Wilbert et al., 2020) and with established principles of microbial community interactions. Actinomyces is frequently observed near the epithelial core, while Rothia typically forms a cortical layer around the consortium. ...
Advances in cellular imaging technologies, especially those based on fluorescence in situ hybridization (FISH) now allow detailed visualization of the spatial organization of human or bacterial cells. Quantifying this spatial organization is crucial for understanding the function of multicellular tissues or biofilms, with implications for human health and disease. To address the need for better methods to achieve such quantification, we propose a flexible multivariate point process model that characterizes and estimates complex spatial interactions among multiple cell types. The proposed Bayesian framework is appealing due to its unified estimation process and the ability to directly quantify uncertainty in key estimates of interest, such as those of inter-type correlation and the proportion of variance due to inter-type relationships. To ensure stable and interpretable estimation, we consider shrinkage priors for coefficients associated with latent processes. Model selection and comparison are conducted by using a deviance information criterion designed for models with latent variables, effectively balancing the risk of overfitting with that of oversimplifying key quantities. Furthermore, we develop a hierarchical modeling approach to integrate multiple image-specific estimates from a given subject, allowing inference at both the global and subject-specific levels. We apply the proposed method to microbial biofilm image data from the human tongue dorsum and find that specific taxon pairs, such as Streptococcus mitis-Streptococcus salivarius and Streptococcus mitis-Veillonella, exhibit strong positive spatial correlations, while others, such as Actinomyces-Rothia, show slight negative correlations. For most of the taxa, a substantial portion of spatial variance can be attributed to inter-taxon relationships.
... Tongue-coating microorganisms constitute a highly diverse and specific microbial population on the back of the tongue and are a crucial part of the body's microbial communities (Kazor et al., 2003;Dewhirst et al., 2010). This flora, predominantly comprising Bacteroides, Fusobacteria, Actinobacteria, and Firmicutes (Wilbert et al., 2020), reflects the majority of the microorganisms in the body. Previous studies have highlighted the significant role of these microorganisms in the formation of tongue coatings, suggesting their potential in disease diagnosis and treatment evaluation. ...
Introduction
Tongue diagnosis, a cornerstone of Traditional Chinese Medicine (TCM), relies significantly on the assessment of tongue coating, which is used to evaluate Zang-fu organ functions, qi and blood dynamics, and the influence of pathogenic factors. This diagnostic method is integral to disease diagnosis and treatment in TCM. Recent research suggests a strong correlation between the characteristics of tongue coating and its microbial composition. These microbial variations may influence the formation and changes in tongue coating and are potentially linked to the progression of specific diseases. However, comprehensive research on the association between tongue coating, its microorganisms, and colorectal cancer (CRC) is limited. Notably, the quantitative aspects of tongue diagnosis and the microbial diversity in tongue coatings across different stages of colorectal cancer (from healthy individuals to colorectal adenoma (CRA) and CRC patients) are yet to be fully elucidated. By studying the cross-population characteristics of tongue image and tongue coating microorganisms during the evolution of colorectal cancer, the differences of tongue image characteristics and tongue coating microorganisms among different populations were further evaluated, providing references for early screening, diagnosis and treatment of colorectal cancer.
Methods
The tongue image features of the subjects were collected by DS01-B tongue surface information collection system, mainly including tongue quality and tongue coating, and the tongue image was quantitatively analyzed by color space Lab value. The microbial characteristics of tongue coating were detected by high-throughput sequencing (16SrRNA amplicon sequencing). All subjects came from the patients in the Sixth Affiliated Hospital of Sun Yat-sen University and recruited volunteers (divided into health group, CRA group and CRC group), and obtained the ethical approval of the Sixth Affiliated Hospital of Sun Yat-sen University (ethical batch number: 2021ZSLYEC-328).
Results
A total of 377 subjects were recruited in this study, including 56 healthy subjects, 65 colorectal adenomas and 256 colorectal cancer patients. The results showed that: in terms of texture of fur, the “thick fur” was a significant statistical difference (p < 0.05) in the 3 groups. In addition, there was also a statistical difference in “greasy fur” and “peeled fur” among the 3 groups (p < 0.05). Lab quantitative analysis of tongue color and fur color: The results showed that the L value of tongue color in healthy group was significantly different from that in CRA group and CRC group (p < 0.01), but there was no significant difference between CRA group and CRC group (p > 0.05). Tongue coating microorganisms, there was no significant difference in the richness and diversity of the three groups of subjects (p > 0.05). There were 296 species in the three groups, accounting for 44.65%, and the species in colorectal cancer population was the most, reaching 502. From the differences in community composition among the three groups, it was found that there were certain differences in bacterial community composition between healthy people, CRA and CRC, and the differences became more and more obvious with the development of the disease.
Conclusion
This study revealed the specific cross-population tongue image characteristics and the specificity of tongue coating microorganisms in the evolution of CRC, providing new research ideas for early screening, early diagnosis, mechanism exploration, prevention and treatment of colorectal cancer.
... Poor oral health may cause an inflammatory response that is strongly associated with heart failure [7,8]. The tongue, as an important part of the oral cavity, plays an important role in oral health, and the dorsum of the tongue carries the largest number of microbial species, which is an important part of oral health [9]. Inflammation and oxidative stress in the body caused by alterations in the oral microbiome are associated with the risk of developing heart failure [10]. ...
Background
Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics.
Objective
The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms.
Methods
Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People’s Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance.
Results
Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model’s capacity to accurately identify participants with HFpEF.
Conclusions
This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care.
Trial Registration
China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133
... Fluorescence in situ hybridization (FISH) with rRNAtargeted probes is a powerful molecular method that enables the direct identification, quantification, and localization of microorganisms within complex microbial communities without prior cultivation [1]. FISH has been widely used to study the abundance and spatial arrangement of microorganisms within biofilms and thereby contributed greatly to revealing their structural organization [2][3][4][5]. In complex microbial systems, however, it is equally important to trace the metabolic activities of microorganisms in situ. ...
Background
Correlative structural and chemical imaging of biofilms allows for the combined analysis of microbial identity and metabolism at the microscale. Here, we developed pH-FISH, a method that combines pH ratiometry with fluorescence in situ hybridization (FISH) in structurally intact biofilms for the coupled investigation of microbial acid metabolism and biofilm composition. Careful biofilm handling and modified sample preparation procedures for FISH allowed preservation of the three-dimensional biofilm structure throughout all processing and imaging steps. We then employed pH-FISH to investigate the relationship between local biofilm pH and the distribution of acid-producing (streptococci) and acid-consuming (Veillonella spp.) bacteria in dental biofilms from healthy subjects and caries-active patients.
Results
The relative abundance of streptococci correlated with low biofilm pH at the field-of-view level, while the opposite trend was observed for Veillonella spp. These results suggest that clusters of streptococci contribute to the formation of acidic pockets inside dental biofilms, whereas Veillonella spp. may have a protective role against biofilm acidification.
Conclusions
pH-FISH combines microscale mapping of biofilm pH in real time with structural imaging of the local microbial architecture, and is a powerful method to explore the interplay between biofilm composition and metabolism in complex biological systems.
3LzzcRAp49PHcLLUMV79SFVideo Abstract
... Tongue image diagnosis plays a central role in TCM diagnosis, with recent research indicating a strong correlation between changes in tongue appearance, coating, and the oral microbiome (Han et al., 2016;Wilbert et al., 2020;Lu et al., 2022). As the oral cavity serves as the entry point to the digestive tract, the oral and gastrointestinal microbiota-the two largest microbiomes in the human body-are intricately connected. ...
Background
This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in diabetes progression.
Methods
Participants included 30 Pre-DM patients, 37 individuals with T2DM, and 28 healthy controls. Tongue images and oral/fecal samples were analyzed using image processing and 16S rRNA sequencing. Machine learning techniques, including support vector machine (SVM), random forest, gradient boosting, adaptive boosting, and K-nearest neighbors, were applied to integrate tongue image data with microbiota profiles to construct predictive models for Pre-DM and T2DM classification.
Results
Significant shifts in tongue characteristics were identified during the progression from Pre-DM to T2DM. Elevated Firmicutes levels along the oral-gut axis were associated with white greasy fur, indicative of underlying metabolic changes. An SVM-based predictive model demonstrated an accuracy of 78.9%, with an AUC of 86.9%. Notably, tongue image parameters (TB-a, perALL) and specific microbiota (Escherichia, Porphyromonas-A) emerged as prominent diagnostic markers for Pre-DM and T2DM.
Conclusion
The integration of tongue diagnosis with microbiome analysis reveals distinct tongue features and microbial markers. This approach significantly improves the diagnostic capability for Pre-DM and T2DM.
... While images of bacteria at cellular resolution in the intestine have allowed observation of the close-packed arrangement of hard-to-access mucosal communities [17], understanding how these microbial communities evolve over time and space remains unclear. This is because high-resolution real-time imaging in live animals is yet impractical [17,18]. Moreover, there is insufficient data on the kinetic properties of individual microbes, as in-vitro measurements often differ significantly from in-vivo kinetic features [7]. ...
Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.
... Green represents correct predictions, and orange represents inconsistent predictions. The overall prediction accuracy of iRM23NL was computed using Equation6. ...
Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model’s effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa’s metabolism for desired performance.
IMPORTANCE
Cystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium’s genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.
... All confocal micrographs were processed to generate reconstructed 3D images using the Imaris (v10.0) software following steps similar to that in previous studies (Wilbert et al., 2020). ...
Numerous studies have investigated the effects of stannous ions on specific microbes and their efficacy in reducing dental plaque. Nonetheless, our understanding of their impact on the oral microbiome is still a subject of ongoing exploration. Therefore, this study sought to evaluate the effects of a stannous-containing sodium fluoride dentifrice in comparison to a zinc-containing sodium fluoride dentifrice and a control group on intact, healthy oral biofilms. Utilizing the novel 2bRAD-M approach for species-resolved metagenomics, and FISH/CLSM with probes targeting periodontal and caries associated species alongside Sn²⁺ and Zn²⁺ ions, we collected and analyzed in situ biofilms from 15 generally healthy individuals with measurable dental plaque and treated the biofilms with dentifrices to elucidate variations in microbial distribution. Although significant shifts in the microbiome upon treatment were not observed, the use of a stannous-containing sodium fluoride dentifrice primarily led to an increase in health-associated commensal species and decrease in pathogenic species. Notably, FISH/CLSM analysis highlighted a marked reduction in representative species associated with periodontitis and caries following treatment with the use of a stannous-containing sodium fluoride dentifrice, as opposed to a zinc-containing sodium fluoride dentifrice and the control group. Additionally, Sn²⁺ specific intracellular imaging reflected the colocalization of Sn²⁺ ions with P. gingivalis but not with other species. In contrast, Zn²⁺ ions exhibited non-specific binding, thus suggesting that Sn²⁺ could exhibit selective binding toward pathogenic species. Altogether, our results demonstrate that stannous ions could help to maintain a healthy oral microbiome by preferentially targeting certain pathogenic bacteria to reverse dysbiosis and underscores the importance of the continual usage of such products as a preventive measure for oral diseases and the maintenance of health.
