CORAL SCAR INVESTIGATION: AN APPLICATION OF MACHINE LEARNING AND COMPUTATIONAL BIOLOGY METHODS TO UNDERSTAND CORAL HOLOBIONT RESPONSE TO VARIOUS TISSUE LOSS DISEASES
Coral disease is one of the biggest challenges facing coral reefs that actively changes biodiversity resulting in coral decline. With the rising threat of diseases, corals require biomarkers that reflect the immune systems and differences between common coral tissue loss diseases to best assist in coral restoration efforts. To obtain these biomarkers, my dissertation leverages two previously published datasets from two tissue loss disease exposure studies to investigate genes that are relevant for coral immune pathways, disease susceptibility, and classification between the diseases. In Chapter 2, I use comparative computational biology tools and protein assays to identify the melanin cascade in stony corals and the primary enzyme responsible for melanin synthesis. Melanin was identified to be a potential constitutive immune trait for corals affected by stony coral tissue loss disease. In Chapter 3, I apply differential expression and machine learning techniques to characterize and classify two tissue loss diseases based on coral host gene expression with a total of 463 biomarkers identified to characterize the two diseases. Finally, in Chapter 4, I apply machine learning methods to characterize and classify tissue loss diseases based on the algal endosymbiont gene expression, where 407 biomarkers were identified for disease classification. Overall, these chapters support the hypothesis that the application of machine learning and computational biology approaches applied to existing coral disease datasets will yield disease classification biomarkers that determine immune patterns or pathways indicative of how the coral holobiont respond to different diseases
