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Additional file 6: Figure S6. Heatmap of taxa identified as the core bacterial community in the (A) 16S rRNA gene amplicon data and the (B) metatranscriptomic data. Green boxes indicate the presence of the core taxa in each sample per site. Core taxa was defined as occurring in > 80% of the samples per sequencing type.

Additional file 5: Figure S5. NMDS plot visualizations of Bray-Curtis beta-diversity (k = 2) at the (A) Species and (B) Order levels for gut metatranscriptomic samples by Site. The ellipse lines show the 95% confidence interval (standard deviation). p-values indicate significance of grouping with adonis2 Permutational Multivariate Analysis of Variance Using Distance Matrices test.

Additional file 8: Figure S8. Differential expression (log fold change) of SEED Level 4 gene annotation of Osmotic and Periplasmic stress response groups at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Additional file 9: Figure S9. Differential expression (log fold change) of SEED level 4 gene annotation of nitrogen metabolism pathways at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Additional file 10: Figure S10. Differential expression (log fold change) of SEED level 4 gene annotation of phosphorus metabolism pathways at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Additional file 3: Figure S3. Number of bacterial Orders shared between the oyster gut and seawater 16S rRNA gene amplicons at each site (vertical bars). The total number of Orders found in each group is shown in the horizontal bar graph on the right. Intersections in gray denote comparisons that include the water samples.

Additional file 4: Figure S4. (A) Linear discriminant analysis Effect Size (LEfSe) analysis of bacterial Orders in seawater (n = 10), compared to gut samples (n = 50) in the 16S rRNA gene amplicons (one-against-all). (B) LEfSe analysis of bacterial Orders in gut samples at each site (n = 10) in the 16S rRNA gene amplicons (all-against-all). Only significantly increased taxa are shown. Significance was determined by LDA score > 2.0, alpha value = 0.05 for factorial Kruskal-Wallis test, and alpha value = 0.05 for pairwise Wilxocon test.

Additional file 1: Figure S1. (A) Percent abundances of the 10 most abundant phyla for 16S rRNA gene amplicon sequencing data by sample type and site. All other taxa are grouped into “Others.” Mock community control samples are shown at the left. (B) Percent abundance of the top 10 most abundant ASVs in the mock community samples. All other taxa are grouped into “Others.”

Additional file 7: Figure S7. Differential expression (log fold change) of SEED Level 4 gene annotation of Oxidative Stress response groups at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Additional file 9: Figure S9. Differential expression (log fold change) of SEED level 4 gene annotation of nitrogen metabolism pathways at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Additional file 4: Figure S4. (A) Linear discriminant analysis Effect Size (LEfSe) analysis of bacterial Orders in seawater (n = 10), compared to gut samples (n = 50) in the 16S rRNA gene amplicons (one-against-all). (B) LEfSe analysis of bacterial Orders in gut samples at each site (n = 10) in the 16S rRNA gene amplicons (all-against-all). Only significantly increased taxa are shown. Significance was determined by LDA score > 2.0, alpha value = 0.05 for factorial Kruskal-Wallis test, and alpha value = 0.05 for pairwise Wilxocon test.

Additional file 10: Figure S10. Differential expression (log fold change) of SEED level 4 gene annotation of phosphorus metabolism pathways at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Additional file 7: Figure S7. Differential expression (log fold change) of SEED Level 4 gene annotation of Oxidative Stress response groups at each site, relative to the mean of the others. All significantly regulated genes are outlined in red and annotated with an asterisk (n = 5, Benjamini-Hochberg *padj

Bivalves, from raw oysters to steamed clams, are popular choices among seafood lovers and once limited to the coastal areas. The rapid growth of the aquaculture industry and improvement in the preservation and transport of seafood have enabled them to be readily available anywhere in the world. Over the years, oysters, mussels, scallops, and clams have been the focus of research for improving the production, managing resources, and investigating basic biological and ecological questions. During this decade, an impressive amount of information using high-throughput genomic, transcriptomic and proteomic technologies has been produced in various classes of the Mollusca group, and it is anticipated that basic and applied research will significantly benefit from this resource. One aspect that is also taking momentum is the use of bivalves as a model system for human health. In this review, we highlight some of the aspects of the biology of bivalves that have direct implications in human health including the shell formation, stem cells and cell differentiation, the ability to fight opportunistic and specific pathogens in the absence of adaptive immunity, as source of alternative drugs, mucosal immunity and, microbiome turnover, toxicology, and cancer research. There is still a long way to go; however, the next time you order a dozen oysters at your favorite raw bar, think about a tasty model organism that will not only please your palate but also help unlock multiple aspects of molluscan biology and improve human health.

Gnotobiotic animal models reconventionalized under controlled laboratory conditions with multi-species bacterial communities are commonly used to study host-microbiota interactions under presumably more reproducible conditions than conventional animals. The usefulness of these models is however limited by inter-animal variability in bacterial colonization and our general lack of understanding of the inter-individual fluctuation and spatio-temporal dynamics of microbiota assemblies at the micron to millimeter scale. Here, we show underreported variability in gnotobiotic models by analyzing differences in gut colonization efficiency, bacterial composition, and host intestinal mucus production between conventional and gnotobiotic zebrafish larvae re-conventionalized with a mix of 9 bacteria isolated from conventional microbiota. Despite similar bacterial community composition, we observed high variability in the spatial distribution of bacteria along the intestinal tract in the reconventionalized model. We also observed that, whereas bacteria abundance and intestinal mucus per fish were not correlated, reconventionalized fish had lower intestinal mucus compared to conventional animals, indicating that the stimulation of mucus production depends on the microbiota composition. Our findings, therefore, suggest that variable colonization phenotypes affect host physiology and impact the reproducibility of experimental outcomes in studies that use gnotobiotic animals. This work provides insights into the heterogeneity of gnotobiotic models and the need to accurately assess re-conventionalization for reproducibility in host-microbiota studies., Competing Interests: Conflict of Interest The authors declare no conflict of interest.