Comparative transcriptomics reveals the molecular toolkit used by an algivorous protist for cell wall perforation
Bibliographic record
Abstract
Summary Microbial eukaryotes display a stunning diversity of feeding strategies, ranging from generalist predators to highly specialised parasites. The unicellular “protoplast feeders” represent a fascinating mechanistic intermediate, as they penetrate other eukaryotic cells (algae, fungi) like some parasites, but then devour their cell contents by phagocytosis. Besides prey recognition and attachment, this complex behaviour involves the local, pre-phagocytotic dissolution of the prey cell wall, which results in well-defined perforations of species-specific size and structure. Yet, the molecular processes that enable protoplast feeders to overcome cell walls of diverse biochemical composition remain unknown. We used the flagellate Orciraptor agilis (Viridiraptoridae, Rhizaria) as a model protoplast feeder, and applied differential gene expression analysis to examine its penetration of green algal cell walls. Besides distinct expression changes that reflect major cellular processes (e.g. locomotion, cell division), we found lytic carbohydrate-active enzymes that are highly expressed and upregulated during the attack on the alga. A putative endocellulase (family GH5_5) with a secretion signal is most prominent, and a potential key factor for cell wall dissolution. Other candidate enzymes (e.g. lytic polysaccharide monooxygenases) belong to families that are largely uncharacterised, emphasising the potential of non-fungal micro-eukaryotes for enzyme exploration. Unexpectedly, we discovered various chitin-related factors that point to an unknown chitin metabolism in Orciraptor , potentially also involved in the feeding process. Our findings provide first molecular insights into an important microbial feeding behaviour, and new directions for cell biology research on non-model eukaryotes.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".