(110c) A Systems Biomedicine Approach to Improve Our Understanding of Pseudomonas Aeruginosa Infections

Pathogen infections are often multi-scale processes, involving pathogen growth, motility, and interaction with various host factors. One such system is the infections of internal epithelial mucus layers of the human body by the antibiotic resistant pathogen Pseudomonas aeruginosa. P. aeruginosa is a leading cause of infections in immunocompromised individuals in healthcare settings. The treatment of these infections is further complicated by the presence of a variety of metabolic and virulence mechanisms among clinical strains. My research involves exploring the metabolic and phenotypic characteristics of P. aeruginosa in the infected mucus layers in various parts of the human body. Specifically, I focus on the microbial interactions with mucin, which is a key protein component in the mucus layer and is known to modulate the pathogen's metabolic traits as well as the proliferation and development of biofilms. Understanding the mechanistic mucin-driven modulations of microbial phenotypes is of paramount importance in multiple diseases including cystic fibrosis, a disease characterized by defective clearance of mucus. However, the mechanisms of the specific modulating effects of mucins are poorly understood.

We hypothesize that the metabolic differences in P. aeruginosa present in patients in hospital settings are dependent on a complex combination of host and pathogen-specific factors, which can be delineated using a combination of genomic and transcriptomic analyses coupled with genome-scale metabolic modeling to identify the core and unique metabolic functions in different clinical isolates. In our recent study, we characterized a set of 25 clinical P. aeruginosa isolates that is representative of a large population of isolates collected from University of Virginia health System. We performed a multi-faceted analysis of these isolates by employing whole genome sequencing, phenotypic and genotypic clustering, functional annotation and analyses on core, accessory, and unique trait in the P. aeruginosa pan-genome, and genome-scale metabolic reconstruction and analysis of flux samples.

In order to assess the impact of mucin, five clinical isolates along with the lab strain PA14 were selected for transcriptomic sequencing using static cultures in Synthetic Cystic Fibrosis growth medium (SCFM) ± 0.5% MUC5AC under aerobic conditions. Purified RNA was subjected to rRNA depletion and high-throughput sequencing. Specific metabolic functions and biological processes modulated by the presence of mucin were identified using differential gene expression analysis. Furthermore, the transcriptomic datasets are being analyzed and incorporated into the genome-scale metabolic models of the clinical isolates; this integration allows us to identify the mucin-driven metabolic changes in the clinical isolates and their differences across the isolates. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and mucin-induced metabolic modulations during infection.

As an extension of the current research, I plan to investigate the disease progression and how disease dynamics operate at a multi-scale level. I will develop and incorporate a multi-scale model of pathogen growth, motility and virulence in biofilm and planktonic states. In addition to that, I will use the high-throughput omics data from patients at different stages of their chronic health conditions and infection to develop the regulation dynamics and construct new computational tools for modeling the different pathogen and host factors and how they interplay together to manifest a specific disease phenotype. This research will guide us to devise innovative solutions for combating this pathogen by controlling biofilm dispersion, which can then be translated to many other infectious diseases.