Adaptive eukaryote‐to‐eukaryote lateral gene transfer: stress‐related genes of algal origin in the closest unicellular relatives of animals
Bibliographic record
Abstract
In addition to mutation, gene duplication and recombination, the transfer of genetic material between unrelated species is now regarded as a potentially significant player in the shaping of extant genomes and the evolution and diversification of life. Although this is probably true for prokaryotes, the extent of such genetic exchanges in eukaryotes (especially eukaryote-to-eukaryote transfers) is more controversial and the selective advantage and evolutionary impact of such events are less documented. A laterally transferred gene could either be added to the gene complement of the recipient or replace the recipient's homologue; whereas gene replacements can be either adaptive or stochastic, gene additions are most likely adaptive. Here, we report the finding of four stress-related genes (two ascorbate peroxidase and two metacaspase genes) of algal origin in the closest unicellular relatives of animals, the choanoflagellates. At least three of these sequences represent additions to the choanoflagellate gene complement, which is consistent with these transfers being adaptive. We suggest that these laterally acquired sequences could have provided the primitive choanoflagellates with additional or more efficient means to cope with stress, especially in relation to adapting to freshwater environments and/or sessile or colonial lifestyles.
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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 | 0.000 | 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".