Genome sequencing reveals metabolic and cellular interdependence in an amoeba-kinetoplastid symbiosis
Bibliographic record
Abstract
Endosymbiotic relationships between eukaryotic and prokaryotic cells are common in nature. Endosymbioses between two eukaryotes are also known; cyanobacterium-derived plastids have spread horizontally when one eukaryote assimilated another. A unique instance of a non-photosynthetic, eukaryotic endosymbiont involves members of the genus Paramoeba, amoebozoans that infect marine animals such as farmed fish and sea urchins. Paramoeba species harbor endosymbionts belonging to the Kinetoplastea, a diverse group of flagellate protists including some that cause devastating diseases. To elucidate the nature of this eukaryote-eukaryote association, we sequenced the genomes and transcriptomes of Paramoeba pemaquidensis and its endosymbiont Perkinsela sp. The endosymbiont nuclear genome is ~9.5 Mbp in size, the smallest of a kinetoplastid thus far discovered. Genomic analyses show that Perkinsela sp. has lost the ability to make a flagellum but retains hallmark features of kinetoplastid biology, including polycistronic transcription, trans-splicing, and a glycosome-like organelle. Mosaic biochemical pathways suggest extensive 'cross-talk' between the two organisms, and electron microscopy shows that the endosymbiont ingests amoeba cytoplasm, a novel form of endosymbiont-host communication. Our data reveal the cell biological and biochemical basis of the obligate relationship between Perkinsela sp. and its amoeba host, and provide a foundation for understanding pathogenicity determinants in economically important Paramoeba.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".