In synthetic chemistry, the direct functionalization of C-H bonds with oxygen-containing groups is a powerful strategy to efficiently synthesize molecules used in materials and therapeutics. However, current methods to accomplish such reactions suffer from limited substrate scope, selectivity, and tolerance towards other functional groups. Iron-dependent enzymes represent a promising solution to this problem, as they are known to mediate a plethora of complex oxygenation reactions in a highly selective fashion while using inexpensive and earth-abundant reagents. In prior work, we found that Fe(II) 2-oxoglutarate dependent hydroxylases (Fe(II)/2OGs) exhibit non-native oxyfunctionalization activity on olefinic amino acids. Here, we explore the ability of Fe(II)/2OGs to catalyze non-native asymmetric oxyfunctionalizations. We plan to evaluate the ability of our library of Fe(II)/2OGs to catalyze oxyfunctionalization of non-native substrates with various functional groups. Subsequently, we will optimize activity using directed evolution to arrive at a highly active and enantioselective enzyme capable of this chemistry.

Disease is a major threat to tropical coral reefs which can be made worse by local stressors like overfishing and nutrient pollution and global stressors such as climate change. However, not all coral species suffer disease at equal rates. It has been hypothesized that these differences may be due to differences in coral innate immune strategies, biogeography, or the symbiotic associations between corals and protective microorganisms (the ‘coral probiotic hypothesis’). This project seeks to test if there are properties of coral microbiomes that correlate with differences in disease susceptibility. We’ve tested this using 1272 coral 16S rRNA gene amplicon libraries and three long-term coral disease datasets. Establishing a general picture of coral disease susceptibility requires integrating data from multiple regional disease monitoring projects. This is challenging because these projects use different methodologies, monitor different species and occasionally use different terminology for the same diseases. I’ve merged three long-term coral disease datasets: the Florida Reef Resilience Project, the Hawai’i Coral Disease database, and an extensive unpublished dataset from Dr. Joleah Lamb. This combined dataset consists of 141 different coral taxa and 31 unique categories of diseases and stressors. This combined disease resource allows for both investigation of the evolutionary history of coral disease susceptibility and comparison against our microbiome data. Our results so far identify coral groups especially susceptible to certain diseases (e.g., Acropora and Skeletal Eroding Band). So far, phylomorphospace analysis indicates an intriguing potential association between microbiome complexity and disease susceptibility. Preliminary results also indicate a strong correlation between microbiome richness and skeletal eroding band disease over more than 450 million years of coral evolution. This new evidence, if confirmed would support the coral probiotic hypothesis.

My project is about automated classification of vicarious trial and error (VTE), a behavior observed in rats when they pause and look around before making decisions during a spatial memory task. During delayed spatial alternation (DSA) tasks, a rat is randomly placed on one of two start arms of a plus maze, with reward delivered on alternating arms for each trial. The movements of rats are recorded as position data while they perform the task.

Since our lab don’t have a commonly agreed upon criteria for VTE classification with our maze, manual scoring of VTE with recorded behavioral data has been time consuming, requiring many people to do the same work. Thus, my mentor and I decided to make a machine learning program to achieve automated and highly efficient VTE classification.

I first produced a representative data set with trials that were manually scored and commonly agreed by our lab members. Then, my mentor and I figured out several quantifiable features of VTEs and non-VTEs based on the representative data set. My mentor and I used machine learning algorithms to let our program learn those features that separate VTEs from not VTEs and help us accomplish automatic classification of VTEs with raw behavioral data from the DSA task. Preliminary result indicates that the supervised classification by the program aligns well with manual scoring, with roughly the same degree of agreement. Thus, I am currently transiting from a fully supervised method to a semi-supervised method, which allows almost full automation and minimal manual oversight. This work will provide insights for the behavioral strategy of rats throughout learning and guide us to find the connection between VTE behavior and neural circuitry.