# Metabolic Basis of Bacterial Community Function in the Cystic Fibrosis Airway

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2021 · $466,513

## Abstract

Abstract. Cystic fibrosis (CF) is a fatal genetic disease characterized by overproduction of mucus in the lungs
followed by chronic lung infections. Conventional wisdom has been that most CF lung infections involve a
single dominant organism, most commonly the pathogenic bacterium Pseudomonas aeruginosa. Advances in
culture-independent techniques have revealed that CF lung infections are rarely mono-microbial and instead
usually involve complex microbial communities, yet the interspecies interactions that drive these communities
are poorly understood. Furthermore, numerous studies have demonstrated that polymicrobial infections are
more difficult than mono-microbial infections to eradicate with antibiotics, leading to the concept of recalcitrant
communities. The mechanisms underlying recalcitrance are thought to involve synergistic interactions between
community members, but very little data are available to understand this phenomenon. Combined with the
realization that many CF patients respond poorly to available antibiotic regimens compels a more detailed
understanding of interspecies interactions and their impacts on antibiotic recalcitrance to improve the treatment
of CF infections, as well as other polymicrobial diseases. Here, we combine big-data bioinformatics, in silico
computational modeling and in vitro culture experiments to gain insights into the metabolic interactions that
drive CF disease outcomes and antibiotic recalcitrance. The research will leverage an available data set of
hundreds of CF patient samples that provide both bacterial composition data and clinical metadata, including
measures of lung function. These samples will be clustered according to their measured compositions and
metabolic capabilities predicted through computational metabolic modeling to test the hypothesis that the vast
complexity of these many bacterial communities can be collapsed into a small number of model communities
that capture most of the observed metabolic variability. These computational predictions will be tested by
developing in vitro cell culture models that recapitulate the most important metabolic features of the in vivo
polymicrobial communities (Aim 1). By applying bioinformatics and modeling to the same clinical data, we will
test the hypothesis that community metabolic features drive disease outcomes and the virulence potential of
these communities (Aim 2). Finally, we will interrogate the clinical data and in vitro communities to test the
hypothesis that community metabolic features drive antibiotic recalcitrance and differentiate community
responsiveness to antibiotics according to these metabolic features (Aim 3). Our research will yield novel
insights into how complex polymicrobial communities are compositionally structured, interact metabolically,
contribute to disease and respond to antibiotics. Moreover, the research will validate in vitro models that offer
the potential for development of novel antimicrobial strategies to better ...

## Key facts

- **NIH application ID:** 10293007
- **Project number:** 1R01AI155424-01A1
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** George A. O'Toole
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $466,513
- **Award type:** 1
- **Project period:** 2021-06-02 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10293007

## Citation

> US National Institutes of Health, RePORTER application 10293007, Metabolic Basis of Bacterial Community Function in the Cystic Fibrosis Airway (1R01AI155424-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10293007. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*