Characterizing gene expression profiles of various tissue states in stony coral tissue loss disease using a feature selection algorithm DOI Creative Commons
Kelsey M. Beavers, Daniela Gutierrez-Andrade, Emily W. Van Buren

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

ABSTRACT Stony coral tissue loss disease (SCTLD) remains a substantial threat to reef diversity already threatened by global climate change. Restoration efforts and effective treatment of SCTLD requires an in-depth understanding its pathogenesis in the holobiont as well mechanisms resistance. Here, we present supervised machine learning framework describe progression major reef-building coral, Montastraea cavernosa , dominant algal endosymbiont, Cladocopium goreaui . Utilizing support vector recursive feature elimination (SVM-RFE) conjunction with differential expression analysis, identify subset biologically relevant genes that exhibit highest classification performance across three types tissues collected from natural environment: apparently healthy on colony, SCTLD-affected lesion colony. By analyzing gene signatures associated these health states both host endosymbiont (family Symbiodiniaceae), key processes involved resistance within holobiont. Our findings further evidence causes dysbiosis between Symbiodinaiceae additionally describes metabolic immune shifts occur transitions diseased state. This offers novel approach accurately assess endangered species brings us closer developing solutions for monitoring intervention. AUTHOR SUMMARY Coral reefs are under increasing due change, rising ocean temperatures outbreaks accelerating degradation. has been particularly destructive, leading widespread mortality Florida’s Reef wider Caribbean since emergence 2014. While cause unknown, rapid decline highlights urgent need innovative approaches threats health. In this study, applied approach, previously used cancer research, symbiotic algae, which relies meet nutritional requirements. patterns representing different states, find affects interactions their symbionts signaling pathways, even colony appears be healthy. study presents applying research could lead new methods combatting SCTLD.

Language: Английский

Characterizing gene expression profiles of various tissue states in stony coral tissue loss disease using a feature selection algorithm DOI Creative Commons
Kelsey M. Beavers, Daniela Gutierrez-Andrade, Emily W. Van Buren

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

ABSTRACT Stony coral tissue loss disease (SCTLD) remains a substantial threat to reef diversity already threatened by global climate change. Restoration efforts and effective treatment of SCTLD requires an in-depth understanding its pathogenesis in the holobiont as well mechanisms resistance. Here, we present supervised machine learning framework describe progression major reef-building coral, Montastraea cavernosa , dominant algal endosymbiont, Cladocopium goreaui . Utilizing support vector recursive feature elimination (SVM-RFE) conjunction with differential expression analysis, identify subset biologically relevant genes that exhibit highest classification performance across three types tissues collected from natural environment: apparently healthy on colony, SCTLD-affected lesion colony. By analyzing gene signatures associated these health states both host endosymbiont (family Symbiodiniaceae), key processes involved resistance within holobiont. Our findings further evidence causes dysbiosis between Symbiodinaiceae additionally describes metabolic immune shifts occur transitions diseased state. This offers novel approach accurately assess endangered species brings us closer developing solutions for monitoring intervention. AUTHOR SUMMARY Coral reefs are under increasing due change, rising ocean temperatures outbreaks accelerating degradation. has been particularly destructive, leading widespread mortality Florida’s Reef wider Caribbean since emergence 2014. While cause unknown, rapid decline highlights urgent need innovative approaches threats health. In this study, applied approach, previously used cancer research, symbiotic algae, which relies meet nutritional requirements. patterns representing different states, find affects interactions their symbionts signaling pathways, even colony appears be healthy. study presents applying research could lead new methods combatting SCTLD.

Language: Английский

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