Eukaryote-to-eukaryote gene transfer pervades the genome evolution of Rhizaria
Bibliographic record
Abstract
Abstract Lateral gene transfer (LGT) has contributed to the genetic makeup of various eukaryotic lineages, yet its prevalence and long-term significance remain poorly understood, particularly for transfers between eukaryotes. Here, we investigate LGT across 29 species of Rhizaria, an ancient and ecologically diverse clade of predominantly free-living, single-celled phagotrophs. Using phylogenetic analyses of over 40,000 gene families complemented by machine learning-based prediction, we estimate that 8–20% of protein-coding genes in contemporary rhizarian genomes were acquired through LGT at various points during their billion-year history, with ~2,000 transfer events shared between at least two species across the rhizarian tree of life. Gene duplications outnumber LGTs across most lineages, yet LGT-derived genes themselves duplicate more frequently than vertically inherited ones, amplifying the genomic impact of each transfer event. Notably, transfers from other eukaryotes outnumber those from prokaryotes and show distinct signatures: prokaryote-derived LGTs are enriched among extracellular proteins, whereas eukaryote-derived LGTs are overrepresented in nuclear and informational processes. Prokaryote-derived LGT genes progressively acquire introns over evolutionary time, confirming their genomic integration and long-term retention. Our findings establish LGT as a pervasive force in rhizarian genome evolution and highlight eukaryote-to-eukaryote transfer as a substantial but often overlooked component of eukaryotic genetic innovation.
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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) | 0.001 | 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".