Machine learning reveals distinct gene expression signatures across tissue states in stony coral tissue loss disease.

Abstract

Stony coral tissue loss disease (SCTLD) has rapidly degraded Caribbean reefs, compounding climate-related stressors and threatening ecosystem stability. Effective intervention requires understanding the mechanisms driving disease progression and resistance. Here, we apply a supervised machine learning approach-support vector machine recursive feature elimination-combined with differential gene expression analysis to describe SCTLD in the reef-building coral Montastraea cavernosa and its dominant algal endosymbiont, Cladocopium goreaui. We analyse three tissue types: apparently healthy tissue on apparently healthy colonies, apparently healthy tissue on SCTLD-affected colonies and lesion tissue on SCTLD-affected colonies. This approach identifies genes with high classification accuracy and reveals processes associated with SCTLD resistance, such as immune regulation and lipid biosynthesis, as well as processes involved in disease progression, such as inflammation, cytoskeletal disruption and symbiosis breakdown. Our findings support evidence that SCTLD induces dysbiosis between the coral host and Symbiodiniaceae and describe the metabolic and immune shifts that occur as the holobiont transitions from healthy to diseased. This supervised machine learning methodology offers a novel approach to accurately assess the health states of endangered coral species, with potential applications in guiding targeted restoration efforts and informing early disease intervention strategies.