Freida Blostein was awarded $10,000 by ITiMS to conduct this project.
Co-mentors: Betsy Foxman and Kelly Bakulski
Early childhood caries (ECC), or dental decay in the primary teeth of children under 6 years of age, is a painful condition which negatively impacts self-esteem and predicts poor oral health throughout the life course (1–3). ECC affects 20% of children aged 2–5 in the United States (4). ECC is a multifactorial condition with social, behavioral, and microbial risk factors (5). The proximate mechanism involves microbial digestion of carbohydrates to enamel-eroding acids (5).
Streptococcus mutans was once considered the causal agent of ECC (5,6). S. mutans is strongly associated with ECC but is not always present in affected children or absent in healthy children. The ecological hypothesis of dental decay posits instead that bacterial communities interact, creating a functional shift to disease (5–7). Using metagenomics, our lab identified co-occurring microbial groups (guilds) which were prospectively associated with ECC and predicted S. mutans detection (8). ECC was also associated with the abundance of genes encoding metabolic and antibiotic functions. Our work suggests microbial interactions predict an ecological succession to cariogenic functions. Disrupting these interactions could prevent ECC.
However, co-occurrence patterns observed in metagenomic data do not necessarily represent functional interactions. Observed correlations between microbes can result from habitat-filtering, in which organisms co-occur due to nutrient availability, rather than co-operation (9–11). Since microbial genes are not always expressed, metagenomic data does not measure ongoing functions but only their potential to be carried out. To address these gaps, I propose using agent-based models and RNA sequencing to test if metabolic interactions and processes explain assembly, succession, and cariogenicity of microbial communities.
Build in silico microbiomes using BacArena and publicly available genome-scale metabolic models.
Vary initial nutrient availability and community members independently and measure impacts on end community composition, metabolic exchanges, and biofilm growth.
Cultivate S. mutans in different oral microbial communities and evaluate its biomass growth.
Measure ongoing gene expression using RNA sequencing on already collected saliva from children with ECC and age-matched controls.
Compare gene expression of a) ECC cases vs controls at diagnosis b) ECC cases vs controls at the visit preceding diagnosis and c) ECC cases at diagnosis vs the preceding visit.
Map gene data from expression data to metabolic reactions and phenotypes using Gene-Protein-Reaction rules and Boolean operators. Compare expression profiles in saliva samples to those in the agent-based model.
This project combines epidemiological data, agent-based models, and laboratory sequencing to better understand co-occurrence and succession in the early-life oral microbiome. Understanding if and how metabolic interactions between bacteria underly the formation of cariogenic oral communities can inform targeted interventions, such as prebiotics.
(the following is the text found in the graphical abstract above)
1. Can co-occurrence & succession patterns be explained by metabolite preferences? or metabolite cross-feeding?
2. Do ongoing metabolic processes associate with dental decay?