Our focus is on identifying pro-inflammatory gene correlates of pathogenesis in human subjects versus animal models and mechanisms of their abatement via vaccines or therapies in infectious disease, including HIV, SARS and COVID-19. Recent studies assessing natural disease course in human patients and a SARS-CoV infection-reinfection model in ferrets identified and confirmed robust SARS immunopathogenesis biomarkers (e.g. CXCL10) and the targeted benefits of steroid therapy in key windows of human disease course. The ferret study moved on to identify correlates of immune protection during SARS challenge in ferrets previously infected with SARS or immunized with a candidate SARS vaccine. Similar studies in WNV, high-path bird flu in ferrets, SIV infection in NHP, and HIV have helped gain an understanding across very different studies of the role of balance between different IFN signaling gene signatures and the inflammasome in regulating the innate and adaptive immune interface in response to inflammatory disease. This foundation of knowledge in IFNs and the inflammasome has now populated our database across many inflammatory disorders or dysregulated immunity and provides an important resource for polling pro-inflammatory gene and cytokine networks, etc., by comparing new data to a wide variety of our published (including other public data) and unpublished data, most recently in mapping out biomarkers of severe acute COVID-19 and its after-effects in those that survive.

To make the best use of these data, I have developed bioinformatic workflows that produce digestible reports that are also standardized and interface with a growing correlate signature database. My goal is to employ high capacity technology and biostatistical/computational tools and translate bioinformatic results for biology and immunological targets and repurposable drug mining.

Our ultra-low-input transcriptomic platform and biological translation helped define pathways for human dendritic cell (DC) development that include sequential origin of DCs from increasingly restricted progenitors that lead to either granulocyte-monocyte-DC progenitors or monocyte-dendritic progenitors, and finally monocytes and three major DC subsets. This approach and resulting developmental and/or cell subset specific datasets have been integrated and applied to studies of the aging innate immune system, drug-induced transcriptional profiling, the developmental programs of CD8+ T cell differentiation during vaccination, as well as the more recent identification of epigenetic silencing in CD4 cells, blood pressure biomarkers in psoriasis, the role of MSCs in incontinence injury, and gender-related T reg modulation in IBD. Collectively, these studies highlight my more senior-level contributions in the broad meta- and translational potential of my workflows and expertise, e.g. prediction of immunotherapy resistance in melanoma patients via novel T cell subsets, in that new collaborative data can be integrated and benchmarked against existing internal and public datasets.