Syntrophic linkage between predatory <i>Carpediemonas</i> and specific prokaryotic populations
Bibliographic record
Abstract
Most anoxic environments are populated by small (<10 μm) heterotrophic eukaryotes that prey on different microbial community members. How predatory eukaryotes engage in beneficial interactions with other microbes has rarely been investigated so far. Here, we studied an example of such an interaction by cultivating the anerobic marine flagellate, Carpediemonas frisia sp. nov. (supergroup Excavata), with parts of its naturally associated microbiome. This microbiome consisted of so far uncultivated members of the Deltaproteobacteria, Bacteroidetes, Firmicutes, Verrucomicrobia and Nanoarchaeota. Using genome and transcriptome informed metabolic network modeling, we showed that Carpediemonas stimulated prokaryotic growth through the release of predigested biomolecules such as proteins, sugars, organic acids and hydrogen. Transcriptional gene activities suggested niche separation between biopolymer degrading Bacteroidetes, monomer utilizing Firmicutes and Nanoarchaeota and hydrogen oxidizing Deltaproteobacteria. An efficient metabolite exchange between the different community members appeared to be promoted by the formation of multispecies aggregates. Physiological experiments showed that Carpediemonas could also benefit from an association to these aggregates, as it facilitated the removal of inhibiting metabolites and increased the availability of prey bacteria. Taken together, our results provide a framework to understand how predatory microbial eukaryotes engage, across trophic levels, in beneficial interactions with specific prokaryotic populations.
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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 | 0.001 | 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".