Comparative Microbiome and Metabolome Analyses of the Marine Tunicate Ciona intestinalis from Native and Invaded Habitats
Bibliographic record
Abstract
Massive fouling by the invasive ascidian Ciona intestinalis in Prince Edward Island (PEI, Canada) has been causing devastating losses to the local blue mussel farms. In order to gain first insights into so far unexplored factors that may contribute to the invasiveness of C. intestinalis in PEI, we undertook comparative microbiome and metabolome studies on specific tissues from C. intestinalis populations collected in invaded (PEI) and native regions (Helgoland and Kiel, Germany). Microbial community analyses and untargeted metabolomics revealed clear location- and tissue-specific patterns showing that biogeography and the sampled tissue shape the microbiome and metabolome of C. intestinalis. Moreover, we observed higher microbial and chemical diversity in C. intestinalis from PEI than in the native populations. Bacterial OTUs specific to C. intestinalis from PEI included Cyanobacteria (e.g., Leptolyngbya sp.) and Rhodobacteraceae (e.g., Roseobacter sp.), while populations from native sampling sites showed higher abundances of e.g., Firmicutes (Helgoland) and Epsilonproteobacteria (Kiel). Altogether 121 abundant metabolites were putatively annotated in the global ascidian metabolome, of which 18 were only detected in the invasive PEI population (e.g., polyketides and terpenoids), while six (e.g., sphingolipids) or none were exclusive to the native specimens from Helgoland and Kiel, respectively. Some identified bacteria and metabolites reportedly possess bioactive properties (e.g., antifouling and antibiotic) that may contribute to the overall fitness of C. intestinalis. Hence, this first study provides a basis for future studies on factors underlying the global invasiveness of Ciona species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".