ArticleG3 (Bethesda, Md.)2024
Novel metagenomics analysis of stony coral tissue loss disease.
Article in G3 (Bethesda, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The chromosomal genome sequence of the maze coral,Wellcome open research · 2026Article
- Stony Coral Tissue Loss Disease Results in Persistent Microbial-Level Disturbances on Coral Reef Ecosystems.Environmental microbiology reports · 2025Article
- Discovery of Peptidic Siderophore Degradation by Screening Natural Product Profiles in Marine-Derived Bacterial Mono- and Cocultures.Biochemistry · 2025Article
- Shifts in the microbiome and virome are associated with stony coral tissue loss disease (SCTLD).ISME communications · 2025Article
- Tank-based bacterial profiling identifies basin-wide white band disease pathogen candidate and no bacterial associations with coral disease resistance.ISME communications · 2025Article
- Microbiomes of three coral species in the Mexican Caribbean and their shifts associated with the Stony Coral Tissue Loss Disease.PloS one · 2024Article
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6 authors.
Funding
Abstract
Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from 4 stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus, Vibrio, was used to demonstrate the pipeline's effectiveness. Our approach revealed both complementary and unique coral microbiome members compared with a prior metagenome analysis of the same dataset.
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