UC Santa Barbara

Bacterial and viral infections represent an urgent and dynamic threat to human health. Infections with antibiotic resistant pathogens are increasing at an alarming rate, and emergent zoonotic viruses have again demonstrated the potential severity of their impact on society during the SARS-CoV-2 pandemic. In this dissertation, we develop unique approaches to both diagnose pathogen identity and treat antibiotic-resistant infections. We first describe a smartphone-based point-of-care pathogen nucleic acid diagnostic. This method accurately and quantitatively diagnoses important infections, including SARS-CoV-2, influenza, and urinary sepsis pathogens, directly from human patient specimens. To address antibiotic resistance, the second part of this dissertation develops testing methods that utilize host microenvironmental signals and more accurately identify effective antibiotics during prescription selection. During an infection, bacterial pathogens frequently adopt physiological changes in response to the host microenvironment, which can sometimes render them more (or less) susceptible to a given antibiotic. The first approach uses host signals (such as sodium bicarbonate) to mimic the host environment within culture media. These conditions identified clinically significant differences in antibiotic susceptibility across a wide variety of human bacterial pathogens, and were predictive of treatment outcome in murine models of sepsis. We next developed a method of testing antimicrobial susceptibility directly in human urine and serum, which provides a more complete assortment of host signals than is available with contrived growth media. This assay also identified many clinically significant differences in susceptibility, and now requires confirmation in murine and human patient infections. Collectively, these molecular diagnostic and antibiotic testing approaches provide additional options for rapid pathogen diagnosis, and selection of effective antimicrobial treatments.