# Investigating the microbial basis of early childhood caries via integrative analysis of metagenomics metatranscriptomics and metabolomics

> **NIH NIH R03** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $148,614

## Abstract

The increasing availability and scale of omics data have revolutionized our ability to
understand complex biological processes underlying health and disease. Such
biologically informed insights are aligned with the notion of precision medicine and have
the potential to improve diagnoses, prevention, and treatment. In the oral health domain,
multiple omics data layers (e.g., genomics, metagenomics, transcriptomics,
metabolomics), intended to capture aspects of otherwise unobservable biology, are
increasingly being collected in oral health studies. However, methods for powerful and
informative integration of information gained from these multiple data layers remains
elusive. The focus of this proposal, early childhood caries (ECC), is the most common
non-communicable childhood disease. ECC is defined as dental decay in children under
the age of 6. It remains a clinical and dental public health problem and confers
substantial and multi-level human and economic impacts. The advent of precision oral
health care, based upon a new, microbially-informed understanding of ECC, is expected
to shed light onto mechanistic aspects of the disease processes and reveal new ways to
prevent it. We propose to develop new statistical methods and machine learning (ML)
strategies that handle high dimensionality and excess zeros in comprehensive
integrative analyses of metagenomics (MTG), metatranscriptomics (MTX), and
metabolomics (MTB) and apply them to characterize the ECC-related supragingival
biofilm dysbioses, biochemical activities, and their interactions in a community-based
sample of 300 preschool-age children, enrolled in a large-scale investigation of early
childhood oral health in North Carolina. We propose to develop statistical methods to
jointly identify regulatory modules across MTG, MTX, and MTB associated with ECC.
Furthermore, we seek to develop interpretable machine learning approaches to predict
binary (e.g., case status) or quantitative ECC experience (e.g., dmfs index) using MTG,
MTX, and/or MTB. We anticipate that our study will provide novel insights into the
microbial basis of ECC, identify microbial and biochemical disease biomarkers, highlight
potential targets for prevention and treatment, and establish a powerful research
platform that can be extended to characterize other microbiome-related diseases.

## Key facts

- **NIH application ID:** 11039116
- **Project number:** 1R03DE034507-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Di Wu
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $148,614
- **Award type:** 1
- **Project period:** 2024-09-20 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11039116

## Citation

> US National Institutes of Health, RePORTER application 11039116, Investigating the microbial basis of early childhood caries via integrative analysis of metagenomics metatranscriptomics and metabolomics (1R03DE034507-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11039116. Licensed CC0.

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