# Population Analysis of Pseudomonas aeruginosa Virulence

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2021 · $549,727

## Abstract

Pseudomonas aeruginosa (PA) causes frequent and severe infections in hospitalized patients. In addition,
the prevalence of multidrug-resistant PA is increasing and is now between 15-30% in many areas. Thus, it is
not surprising that the IDSA, WHO, and the CDC have each listed PA as serious public health priority in need
of new therapeutic agents. An age-old question concerning PA is why some strains cause substantially more
aggressive infections than others. The recent application of next generation sequencing platforms to this
problem has begun to provide an explanation by demonstrating that PA genomes differ substantially from
strain to strain. Approximately 10-15% of the genes in a typical PA strain are "accessory," meaning that they
are present in some strains but not others. Likewise, the "core" or conserved genome contains numerous
single nucleotide variants (SNVs) and small insertion-deletions (indels). Although a few of these accessory
genes and core alleles have been characterized and shown to modulate virulence, they represent the "tip of
the iceberg." A systematic examination of strain-to-strain differences in PA is likely to uncover a wealth of novel
virulence-impacting genes and alleles. Identification of these would have several important consequences: (i)
They would dramatically enhance our understanding of PA virulence and the mechanisms by which this
bacterium causes severe disease; and (ii) they would allow one to predict the virulence of PA strains based on
the complement of accessory and core virulence genes/alleles that were present in their genomes.
 We hypothesize that application of comparative genomic approaches to large numbers of PA isolates will
identify novel virulence genes/alleles and allow the generation of machine learning models to predict the
virulence of PA isolates based on their genomes. We will perform the following specific aims to test these
hypotheses: (1) Use machine learning models to predict the virulence of PA isolates based upon their
genomic content. (2) Identify accessory genes and core genome SNVs/indels that play a causal role in
virulence. (3) Develop a genome-based model that predicts clinical outcomes in patients with PA
bloodstream infections. The impact of this proposal is twofold. First, it will lay the foundation for future
models that provide valuable prognostic information to clinicians treating PA-infected patients. Second, it will
identify new PA virulence factors that mediate novel pathogenic mechanisms of infection.

## Key facts

- **NIH application ID:** 10367782
- **Project number:** 2R01AI118257-06A1
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** ALAN R HAUSER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $549,727
- **Award type:** 2
- **Project period:** 2015-12-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10367782

## Citation

> US National Institutes of Health, RePORTER application 10367782, Population Analysis of Pseudomonas aeruginosa Virulence (2R01AI118257-06A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10367782. Licensed CC0.

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