Abstract
With the threat of a “post-antibiotic era” looming, there is a critical need to develop new strategies to treat and rapidly diagnose bacterial infections. In addition to clinical challenges associated with antimicrobial resistance, infections can be complicated by a variety of patient-centric factors (e.g. comorbidities, previous antimicrobial treatments), environmental qualities (e.g. nutrient availability, competing microbes), and pathogen-specific phenotypes (e.g. mucoid colony morphology). A more holistic understanding of the interplay between these complicating variables could improve our ability to translate cutting-edge research into the clinic. In this dissertation, I reframe the study of infectious diseases from the perspective of a systems bioengineer. Through three distinct data-driven projects (Chapters 2-4), I discover high-level relationships between antimicrobial resistance, virulence, coinfection, and metabolism across different in vivo and in vitro contexts in the human pathogen, Pseudomonas aeruginosa. In Chapter 2, I uncover complex associations between antimicrobial resistance, virulence-linked morphologies, and source (e.g. lung, urine) in a large and variable collection of clinical P. aeruginosa isolates. These findings motivate the clinical surveillance of pathogen morphology and emphasize key challenges of personalized treatment of infectious diseases. In Chapter 3, I infer competitive metabolic interactions between P. aeruginosa and the pathogen Staphylococcus aureus in a lung-like environment. Consideration of metabolic pathogen-pathogen interactions may aid in the treatment of coinfections. Additionally, I propose metabolites uniquely produced by P. aeruginosa as potential diagnostic biomarkers of infection. In Chapter 4, I use a combined experimental and computational approach to study the interconnectivity of antimicrobial resistance and pathogen metabolism in lab-evolved antimicrobial-resistant strains of P. aeruginosa. Together, this dissertation lays the framework to better leverage clinical data, pathogen-pathogen interactions, and pathogen metabolism in the design of novel treatments and diagnostic biomarkers for bacterial infections.
