From a budding biochemist to a computational scientist, while my training has never been linear, I have always been fascinated by the interactions between plants and microbes. As an undergrad, I worked toward characterizing virulence genes in a Gram-positive plant pathogen known as Rhodococcus fascians. As a Ph.D. student, I wanted to continue working on understudied Gram-positive plant pathogens and was mentored by Dr. Gitta Coaker to understand the evolution and genetic drivers of pathogen host range in the Clavibacter genus. In addition, my Ph.D. was during the heart of the COVID-19 pandemic, so I also became interested in studying natural variation of bacterial-derived epitopes from thousands of bacterial genomes and their interactions with the plant immune system.
One such epitope-receptor pair that I wanted to continue working on is an immune-inducing ligand known as csp22 from cold-shock proteins. Cold-shock proteins are ubiquitous across bacterial genera and are recognized by the surface-localized protein receptor known as CORE. The csp22 receptor CORE is restricted to the Solanaceous family, which contains hundreds of species; however, other plant species outside of Solanaceae are found to respond to csp22 epitopes, though they do not encode a CORE homolog. Currently, we do not have a great understanding of the evolution of CORE, other convergently evolved csp22-receptors, and their receptor perception capabilities. With tens to thousands of receptors encoded within land plants, how do we better understand ligand-receptor interactions at scale and potentially engineer de novo receptors. Using csp22 eptiope variants and their associated receptors as a model, our goal is to characterize receptor evolution, functional plant immune outcomes, and build machine learning models from these data to vastially speed up prediction of receptor utility, discovery, and engineering for emerging pathogens of plants.
