Tissue-Specific Microbiomes of the Red Sea Giant Clam Tridacna maxima Highlight Differential Abundance of Endozoicomonadaceae

Susann Rossbach, Anny Cardenas, Gabriela Perna, Carlos M. Duarte, Christian R. Voolstra

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Giant clams (subfamily Tridacninae) are prevalent members of coral reef communities and engage in symbioses with algal photosymbionts of the family Symbiodiniaceae, similar to their scleractinian coral counterparts. However, we know little about their associated bacterial microbiome members. Here, we explored bacterial community diversity of digestive system, gill, and mantle tissues associated with the giant clam Tridacna maxima across a cross-shelf gradient (inshore, midshore, and offshore reef sites) in the central Red Sea using 16S rRNA gene amplicon sequencing. Different tissues harbor spatially stable and distinct microbial communities. Notably, diverse assemblages of bacteria affiliated to the family Endozoicomonadaceae were prevalent in all tissues, but particularly abundant in gills and to a lesser extent in digestive tissues. Besides Endozoicomonadaceae, bacteria in the families Pasteurellaceae, Alteromonadaceae, and Comamonadaceae were common associates, depending on the tissue queried. Taxonomy-based functional inference identified processes related to nitrogen cycling (among others) to be enriched in giant clam tissues and contributed by Endozoicomonadaceae. Our study highlights the tissue-specificity and broad taxonomic range of Endozoicomonadaceae associates, similar to other marine invertebrates, and suggests their contribution to nitrogen-related pathways. The investigation of bivalve-associated microbiome communities provides an important addition to the pathogen-focused studies for commercially important bivalves (e.g., oysters).
Original languageEnglish (US)
JournalFrontiers in Microbiology
StatePublished - Nov 26 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1071-01-01, BAS/1/1011-01-01
Acknowledgements: This research was funded by King Abdullah University of Science and Technology (KAUST) through baseline funding to CD (BAS/1/1071-01-01) and CV (BAS/1/1011-01-01).


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