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Novel metagenomics analysis of stony coral tissue loss disease.
Heinz, Jakob M; Lu, Jennifer; Huebner, Lindsay K; Salzberg, Steven L; Sommer, Markus; Rosales, Stephanie M.
  • Heinz JM; Center for Computational Biology, Johns Hopkins University; Baltimore, MD 21211, United States.
  • Lu J; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering; Baltimore, MD 21218, United States.
  • Huebner LK; Center for Computational Biology, Johns Hopkins University; Baltimore, MD 21211, United States.
  • Salzberg SL; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering; Baltimore, MD 21218, United States.
  • Sommer M; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States.
  • Rosales SM; Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission; St. Petersburg, FL 33701, United States.
bioRxiv ; 2024 Jun 05.
Article en En | MEDLINE | ID: mdl-38260425
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 four 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 to a prior metagenome analysis of the same dataset.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Año: 2024 Tipo del documento: Article