Microbiome of the freshwater sponge <i>Ephydatia muelleri</i> shares compositional and functional similarities with those of marine sponges
Bibliographic record
Abstract
Sponges are known for hosting diverse communities of microbial symbionts, but despite persistent interest in the sponge microbiome, most research has targeted marine sponges; freshwater sponges have been the focus of less than a dozen studies. Here, we used 16 S rRNA gene amplicon sequencing and shotgun metagenomics to characterize the microbiome of the freshwater sponge Ephydatia muelleri and identify potential indicators of sponge-microbe mutualism. Using samples collected from the Sooke, Nanaimo, and Cowichan Rivers on Vancouver Island, British Columbia, we show that the E. muelleri microbiome is distinct from the ambient water and adjacent biofilms and is dominated by Sediminibacterium, Comamonas, and unclassified Rhodospirillales. We also observed phylotype-level differences in sponge microbiome taxonomic composition among different rivers. These differences were not reflected in the ambient water, suggesting that other environmental or host-specific factors may drive the observed geographic variation. Shotgun metagenomes and metagenome-assembled genomes further revealed that freshwater sponge-associated bacteria share many genomic similarities with marine sponge microbiota, including an abundance of defense-related proteins (CRISPR, restriction-modification systems, and transposases) and genes for vitamin B12 production. Overall, our results provide foundational information on the composition and function of freshwater sponge-associated microbes, which represent an important yet underappreciated component of the global sponge microbiome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".