Multi-‘omic analysis of stony coral tissue loss disease resistance in restoration genotypes of Orbicella faveolata

Stony coral tissue loss disease (SCTLD) has devastated Florida’s Coral Reef since 2014, affecting many endangered coral species and particularly Orbicella faveolata. While there exists anecdotal evidence of disease resistance in O. faveolata populations, no study to date has quantitatively assessed the potential for certain genotypes to survive the SCTLD epidemic. Previous efforts have focused on field experiments, which cannot standardize disease exposure and often have covarying impacts of environmental variability and non-disease-associated mortality. With previous support from FDEP (CPR C2002; Muller et al., 2023), a collaborative team from Mote Marine Laboratory, University of Miami Rosenstiel School, and NOAA’s Atlantic Oceanographic and Meteorological Laboratory conducted the largest coral disease transmission study to date (170 putative genotypes, 345 total fragments, 38% with >2 replicates) using O. faveolata genotypes from Mote’s land-based nursery. This study also prioritized sampling of corals at multiple time points, including initial, pre-exposure, early exposure, initial lesion signs, and >10% tissue mortality to better understand disease responses and progression using multi-‘omic analyses. A total of 2,565 ‘omics samples were collected for population genomics, microbial genomics, transcriptomics, and histological analyses. This comprehensive sampling approach facilitated the greatest possible examination of molecular responses for any coral disease to date. In this current project, we analyzed these multi-’omic datasets, specifically to address the following goals: to 1) screen nursery-reared O. faveolata genotypes for SCTLD resistance profiles using updated genome and transcriptome assemblies, 2) evaluate the natural evolutionary adaptation of O. faveolata microbial communities to SCTLD resistance, and 3) develop a SCTLD susceptibility hierarchy of restoration genotypes combining transmission and genetic datasets.