Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Healthcare Genetics

Committee Chair/Advisor

Tracy Fasolino

Committee Member

Diana Ivankovic

Committee Member

Nicole J. Davis

Committee Member

Margaret Wetsel


Urinary tract infections (UTIs) are one of the most common infectious clinical entities in both community and hospital settings. They have a broad range of clinical severity yet inflict large epidemiological burden of morbidity and mortality on patients and the healthcare system with billions of dollars in cost of treatment. Understanding what methods are optimal for diagnosing UTIs are critical to mitigate the marked impact and cost of these infections.

Chapter 1 and 2 in this work surveys the broad array of diagnostic modalities for UTIs and highlights their advantages and limitations in the context of the current standard of clinical care. This work highlights the key notion that among UTI diagnostic approaches is the ability to identify bacterial resistance rapidly in order to inform treatment decisions. Chapter 3 utilizes the understanding of clinical UTI diagnostics defined in Chapter 2 to examine the molecular mechanisms of antimicrobial resistance. Finally, Chapter 4 leverages understanding of the current limitations of clinical care and genetic mechanisms of bacterial resistance to design a novel multiple UTI antibiotic resistance gene detection assay that may greatly impact patient outcomes and improve antibiotic stewardship. Chapter 5 collates and culminates this dissertation for future research endeavors in the field in the context of healthcare genetics and genomics.

Collectively, this work puts forth a framework to integrate understanding of UTIs across clinical, microbiologic, diagnostic, and treatment domains to improve clinical care for patients and maintain antibiotic effectiveness through early detection and prevention of resistance development.


This work describes molecular characterization of antibiotic resistance genes via multiplex real-time PCR for a diagnostic algorithm to identify polymicrobial urinary tract infections.

Available for download on Friday, May 31, 2024