(597c) Multi-Scale Modeling of Mucin-Driven Modulations of Microbial Phenotypes

Pseudomonas aeruginosa is the leading cause of nosocomial infections and well-known for infecting patients with cystic fibrosis. These infections can be complicated by a variety of virulence mechanisms and a robust metabolic functionality repertoire. This study focuses on the microbial interactions with mucin, which is a key 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. While it is appreciated that mucus provides a critical barrier against microbial pathogens in the lung, gastrointestinal tract, vagina, and several other body surfaces, the specific molecular interaction between mucins (the primary component of mucus) and microbes is poorly understood.

Genome-scale metabolic reconstructions are powerful tools to simulate microbial metabolism and interactions with molecular modulators in infection environment for in silico hypothesis testing of regulatory mechanisms and potentially devising treatment strategies. In this work, to improve our understanding of mucin-driven metabolic phenomena in infectious biofilm, we use a computational framework involving a genome-scale metabolic reconstruction of Pseudomonas aeruginosa. The model identified key modulators of metabolic behavior of the clinically relevant phenotypes of Pseudomonas, specifically key genes and pathways regulated by different mucin molecules. This research will guide us to devise innovative solutions for combating this pathogen, which can then be translated to many other infectious diseases.