UC San Diego

Monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on surfaces is emerging as an important tool for identifying past exposure to individuals shedding viral RNA. Because humans are a dominant source of microbial input into built environment samples, and because SARS-CoV-2 is known to modify the human microbiome, better technologies for reading out both SARS-CoV-2 specifically and the microbiome in general are required. Because the Knight lab has focused on obtaining bacterial, archaeal, and fungal communities from built environment biospecimens for the last 15 years, and because of my own experience working with surface samples from schools as part of the SASEA (Safer at School Early Alert) program, my thesis focuses on improving methods for SARS-CoV-2 detection from built environment specimens, and relating these results to the rest of the microbiome as determined by more established methods such as 16S rRNA amplicon sequencing using the Earth Microbiome Project protocols, developed in the Knight lab.In order to develop practical methods for obtaining SARS-CoV-2 loads on surfaces that are applicable not only to schools (the focus of SASEA) but to other settings including residential elder care, hospitals, and prisons, my thesis takes a systems engineering approach to surface sampling. Specifically, we need to know how to sample (including solving issues with signal persistence), how to perform the biospecimen collection and molecular assays, and where in the environment to sample to optimize signal.