# Guided multiplex analysis of microoxic fitness factors in P. aeruginosa

> **NIH NIH R21** · DARTMOUTH COLLEGE · 2024 · $245,938

## Abstract

Pseudomonas aeruginosa (Pa), a common pathogen, causes a wide range of diverse infections that
lead to enormous health care costs and unacceptably high morbidity and mortality. Pa drug resistance
is an increasing problem. Thus, new Pa infection prevention, elimination and mitigation strategies are
needed. In many infection contexts, including biofilms, infection sites, and multispecies communities,
Pa is often in microoxic environments and microoxia imposes unique challenges to Pa energy
generation, catabolism, and biosynthesis, signaling, and stress responses among other processes. Yet,
microoxic fitness determinants are poorly understood. To enhance our ability to identify genes involved
in microoxic fitness and conditions that will best reveal microoxic fitness phenotypes, we will leverage
our prior work on the analysis of transcriptional signatures using publicly available data. Pa has been
extensively studied using transcriptomics with over 4000 whole transcriptome microarray and RNA-seq
datasets in public repositories. The Hogan Lab in collaboration with the laboratory of Dr. Casey Greene
(University of Colorado, Anschutz) has used these transcriptome datasets to create and validate
normalized compendia for cross-experiment comparisons and developed machine learning
approaches to facilitate the use of these data to identify genes with correlated expression patterns.
Using these resources, we can observe robust co-expression patterns that may not stand out in single
experiments. We found that known microoxic fitness genes are significantly correlated in expression
patterns with each other and with a large set of uncharacterized genes. In Aim 1, we will construct
mutants in candidate microoxic fitness genes, analyze their fitness in microoxic and normoxic
conditions, and compare the phenotype observed using single mutants to the results from a Tn-Seq
performed in similar conditions. In light of our data that show that medium composition and strain
background can influence the expression level of microoxic fitness genes even when cell density and
O2 availability are constant, in Aim 2, we will use transcriptome compendia to identify conditions in
which microoxic fitness genes are most highly expressed. We will use those conditions to assess the
phenotypes of mutants in candidate microoxic fitness genes. Prioritized hits will be validated by
complementation and further characterized in future work. Upon completion of these studies, we will
have expanded our knowledge of microoxic fitness determinants, and we will have tested a strategy for
leveraging public transcriptomics data for identifying candidate genes and assay conditions that might
be broadly useful in other systems and for other processes.

## Key facts

- **NIH application ID:** 10851894
- **Project number:** 5R21AI174132-02
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** DEBORAH A HOGAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $245,938
- **Award type:** 5
- **Project period:** 2023-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851894

## Citation

> US National Institutes of Health, RePORTER application 10851894, Guided multiplex analysis of microoxic fitness factors in P. aeruginosa (5R21AI174132-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10851894. Licensed CC0.

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