# In vitro reproducible model of subgingival microbiome for high throughput screening for microbiome modulators

> **NIH NIH R03** · TEMPLE UNIV OF THE COMMONWEALTH · 2020 · $164,165

## Abstract

Project summary
Periodontitis continues to be a global health problem: combined with edentulism and severe tooth loss, it
constitutes the 6th most prevalent long-term disease worldwide, accounting for 11 million years lived with
disability and lost productivity of 117 billion USD. Currently, periodontitis is viewed as an immunological
destruction of the periodontium, orchestrated by low abundance pathogens in an unbalanced subgingival
microbial community--the so called microbial dysbiosis hypothesis. This entails that selectively targeting
pathogens or/and stimulating growth of commensals to reverse subgingival microbial dysbiosis (or promote
normobiosis) represents a promising strategy for prevention and adjunctive treatment of periodontitis. Such
microbiome modulation can be achieved by using agents like prebiotics and probiotics. Remarkably, while a
number of in vitro dental biofilm/microbiome models has been described in the literature, none has been
developed for the purpose of exploring microbiome modulators. This two-year R03 has a single aim: to develop
a robust, high- throughput, reproducible in vitro subgingival microbiome model specifically optimized for testing
of microbiome modulators. The model will include a dysbiotic (experimental) microbiome grown from
periodontitis-associated subgingival samples, and a normobiotic (reference) microbiome grown from health-
associated subgingival plaques samples. The growth conditions will be fine-tuned to maximize similarity
between the in vitro microbiomes and the original samples. We will also explore the possibility of reproducing
the generated microbiomes by passaging or using frozen stocks, eliminating the need to obtain more patient
samples. In addition, a novel subgingival microbial dysbiosis index (SMDI) as a measure of dysbiosis in the
microbiomes will be developed. The microbial composition of the microbiomes as well as original samples will
be assessed using 16S rRNA sequencing coupled with our BLASTN-based, species-level taxonomy
assignment algorithm. Microbiomes will be compared using distance matrices, principle component analysis
and a similarity index. The model characterized here will provide the scientific community with an important tool
to screen large numbers of candidate modulators and quantitatively assess their effects on subgingival
microbiome, before they can be considered for further testing in animals, and eventually, humans.

## Key facts

- **NIH application ID:** 9973103
- **Project number:** 5R03DE028379-02
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Nezar Al-Hebshi
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $164,165
- **Award type:** 5
- **Project period:** 2019-07-05 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9973103

## Citation

> US National Institutes of Health, RePORTER application 9973103, In vitro reproducible model of subgingival microbiome for high throughput screening for microbiome modulators (5R03DE028379-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9973103. Licensed CC0.

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