Frontiers in Marine ScienceSystems BiologyPeer-reviewed journal article
Metabolomics of healthy and stony coral tissue loss disease affected Montastraea cavernosa corals
Jessica M. DeutschiD, Olakunle A. JaiyesimiiD, Kelly A. PittsiD, Jay Houk, Blake UshijimaiD, Brian K. WalkeriD, Valerie Jean PauliD, Neha GargiD
Abstract
Stony coral tissue loss disease, first observed in Florida in 2014, has now spread along the entire Florida Reef Tract and on reefs in many Caribbean countries. The disease affects a variety of coral species with differential outcomes, and in many instances results in whole-colony mortality. We employed untargeted metabolomic profiling of Montastraea cavernosa corals affected by stony coral tissue loss disease to identify metabolic markers of disease. Herein, extracts from apparently healthy, diseased, and recovered corals, Montastraea cavernosa, collected at a reef site near Ft. Lauderdale, Florida were subjected to liquid-chromatography mass spectrometry-based metabolomics. Unsupervised principal component analysis reveals wide variation in metabolomic profiles of healthy corals of the same species, which differ from diseased corals. Using a combination of supervised and unsupervised data analyses tools, we describe metabolite features that explain variation between the apparently healthy corals, between diseased corals, and between the healthy and the diseased corals. By employing a culture-based approach, we assign sources of a subset of these molecules to the endosymbiotic dinoflagellates, Symbiodiniaceae. Specifically, we identify various endosymbiont-specific lipid classes, such as betaine lipids, glycolipids, and tocopherols, which differentiate samples taken from apparently healthy corals and diseased corals. Given the variation observed in metabolite fingerprints of corals, our data suggests that metabolomics is a viable approach to link metabolite profiles of different coral species with their susceptibility and resilience to numerous coral diseases spreading through reefs worldwide.
- organism: non-human primate
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
- Citations
- 24
- FWCI
- 2.29
- Field percentile
- top 12%
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