Multi-omics Analysis Reveals Important Role for Microbial-derived Metabolites from <i>Botryllus schlosseri</i> in Metal Interactions
Bibliographic record
Abstract
Abstract Marine microbial communities govern many of the biological and chemical processes in the ocean, including element cycles, ecosystem health, and disease. Marine organisms are surrounded by microbes and complex molecular interactions occur between bacterial symbionts, eukaryotic hosts, and their pathogens or prey. Trace metals in the ocean can be either beneficial or detrimental to marine life depending on their concentrations and bioavailability. Multiple marine tunicate species are known to bioaccumulate trace metals in their mantel, and research suggests tunicate microbiota plays an important role in this process. Botryllus schlosseri , a marine colonial tunicate, has become a model organism for cellular and developmental studies, yet its ecological interactions are still not well understood. Using an integrated multidisciplinary approach, we established a comprehensive baseline and explored correlations between members of the B. schlosseri microbiome, metabolome, and metallome to elucidate the ecological effects of trace metals in host-microbe-pathogen interactions. We identified significant correlations between metals, including manganese, nickel, cerium, zinc, and cobalt, with various metabolites and bacterial taxa. These findings offer insights into B. schlosseri biological and chemical interactions with their symbionts and their environment, contributing to bridging the knowledge gap of host-microbiome-environment interactions and establishing a foundation for continuing research on the ecological effects of trace metals in these biological systems. Graphical Abstract Highlights Botryllus schlosseri tissue was highly enriched in metals compared to seawater B. schlosseri microbiome ß-diversity significantly different from seawater Pan-metabolome indicated microbial metabolites in core and flexible metabolome Multi-omics revealed interactions between metals, metabolites, and microbes
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".