Rampant caries is identified by rapid onset, severe decay affecting multiple surfaces, and early pulp infection. This case-control study was conducted to investigate the disparities in oral microbiota between children affected by rampant caries and their caries-free counterparts. A total of 88 preschool children, with matched distribution of sex and age in both the case and control groups, participated in this study. Children's oral health-related behaviors were reported by parents, salivary pH levels were assessed using a portable pen-type pH meter, and supragingival dental plaque was analyzed by 16S rRNA gene sequencing. Children with rampant caries exhibited lower salivary pH levels, poorer toothbrushing habits, and more frequent consumption of sugary snacks. Veillonella, enriched in caries-free children, showed a positive correlation with salivary pH levels and a negative correlation with candy consumption. Conversely, Fusobacterium and Neisseria, more abundant in children with rampant caries, positively correlated with the frequency of candy consumption. Furthermore, Streptococcus mutans, Porphyromonas gingivalis, and Bacteroides acidifaciens were identified as potential oral microbiome markers for differentiating preschoolers with rampant caries from their caries-free peers. B. acidifaciens, typically found in the gut, has been rarely reported in the field of oral health. More well-designed cohort studies are recommended to elucidate the mechanisms through which gut microbiota influences rampant caries in pediatric patients and offer insights into effective strategies for caries management in young children. KEY POINTS: • Lower salivary pH levels in children with rampant caries. • Biomarkers for predicting rampant caries. • Impact of oral health-related behaviors on oral microbiota.

Describe associations between dental caries and dental plaque microbiome, by dentition and family membership.

This cross-sectional analysis included 584 participants in the Center for Oral Health Research in Appalachia Cohort 1 (COHRA1). We sequenced the 16S ribosomal RNA gene (V4 region) of frozen supragingival plaque, collected 10 y prior, from 185 caries-active (enamel and dentinal) and 565 caries-free (no lesions) teeth using the Illumina MiSeq platform. Sequences were filtered using the R DADA2 package and assigned taxonomy using the Human Oral Microbiome Database.

Microbiomes of caries-active and caries-free teeth were most similar in primary dentition and least similar in permanent dentition, but caries-active teeth were significantly less diverse than caries-free teeth in all dentition types. Streptococcus mutans had greater relative abundance in caries-active than caries-free teeth in all dentition types (P < 0.01), as did Veillonella dispar in primary and mixed dentition (P < 0.01). Fusobacterium sp. HMT 203 had significantly higher relative abundance in caries-free than caries-active teeth in all dentition types (P < 0.01). In a linear mixed model adjusted for confounders, the relative abundance of S. mutans was significantly greater in plaque from caries-active than caries-free teeth (P < 0.001), and the relative abundance of Fusobacterium sp. HMT 203 was significantly lower in plaque from caries-active than caries-free teeth (P < 0.001). Adding an effect for family improved model fit for Fusobacterium sp. HMT 203 but notS. mutans.

The diversity of supragingival plaque composition from caries-active and caries-free teeth changed with dentition, but S. mutans was positively and Fusobacterium sp. HMT 203 was negatively associated with caries regardless of dentition. There was a strong effect of family on the associations of Fusobacterium sp. HMT 203 with the caries-free state, but this was not true for S. mutans and the caries-active state.

Patients' and dentists' concerns about transmission of bacteria within families causing caries should be tempered by the evidence that some shared bacteria may contribute to good oral health.

The oral microbiome can be shifted if the patients wear the acrylic retainers for a lengthy period. It is essential to understand the components of the plaque in order to forestall the development of dental caries and gingivitis. The aim of this study is to report the bacterial communities that adhere to the acrylic retainers by full-length nanopore 16S sequencing. Six healthy participants were allocated into 2 groups (chemical tablet and brushing groups). Plaque samples were collected from the acrylic retainer surfaces before and after cleaning. The bacterial communities were reported using full-length nanopore 16S sequencing. The results showed that 7 distinct phyla were identified by sequencing. The most prevalent of these was the Firmicutes. We found a total of 72 genera. The most common microorganism across all samples was Streptococcus, followed by Neisseria, Rothia, and Gemella. The beta diversity showed a significant difference between before and after cleaning (p < 0.05). This study revealed the novel finding that a combination of chemical and mechanical cleaning methods was the most effective method of eliminating retainer biofilms. Moreover, retainer cleaning tablets did not alter the homeostatic balance of the bacterial communities adhering to the acrylic retainers.

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

Identification of complex multidimensional interaction patterns within microbial communities is the key to understand, modulate, and design beneficial microbiomes. Every community has members that fulfill an essential function affecting multiple other community members through secondary metabolism. Since microbial community members are often simultaneously involved in multiple relations, not all interaction patterns for such microorganisms are expected to exhibit a visually uninterrupted pattern. As a result, such relations cannot be detected using traditional correlation, mutual information, principal coordinate analysis, or covariation-based network inference approaches.

We present a novel pattern-specific method to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relation patterns between abundance profiles of two organisms as well as extend this approach to allow search and visualize three-, four-, and higher dimensional patterns. The proposed approach has been tested using 2380 microbiome samples from the Human Microbiome Project resulting in body site-specific networks of statistically significant 2D patterns as well as revealed the presence of 3D patterns in the Human Microbiome Project data.

The presented study suggested that search for Boolean patterns in the microbial abundance data needs to be pattern specific. The reported presence of multidimensional patterns (which cannot be reduced to a combination of two-dimensional patterns) suggests that multidimensional (multi-organism) relations may play important roles in the organization of microbial communities, and their detection (and appropriate visualization) may lead to a deeper understanding of the organization and dynamics of microbial communities. Video Abstract.

The oral microbiome can alter the balance between health and disease, locally and systemically. Within the oral cavity, bacteria, archaea, fungi, protozoa, and viruses may all be found, each having a particular role, but strongly interacting with each other and with the host, in sickness or in health. A description on how colonization occurs and how the oral microbiome dynamically evolves throughout the host's life is given. In this chapter the authors also address oral and nonoral conditions in which oral microorganisms may play a role in the etiology and progression, presenting the up-to-date knowledge on oral dysbiosis as well as the known underlying pathophysiologic mechanisms involving oral microorganisms in each condition. In oral pathology, oral microorganisms are associated with several diseases, namely dental caries, periodontal diseases, endodontic infections, and also oral cancer. In systemic diseases, nonoral infections, adverse pregnancy outcomes, cardiovascular diseases, and diabetes are among the most prevalent pathologies linked with oral cavity microorganisms. The knowledge on how colonization occurs, how oral microbiome coevolves with the host, and how oral microorganisms interact with each other may be a key factor to understand diseases etiology and progression.