Pangenome biology and evolution in harmful algal-bloom-forming pelagophyte algae
Bibliographic record
Abstract
ABSTRACT In prokaryotes lateral gene transfer (LGT) is a key mechanism leading to intra-species variability in gene content and the phenomenon of pangenomes. In microbial eukaryotes, however, the extent to which LGT-driven pangenomes exist is unclear. Pelagophytes are ecologically important marine algae that include Aureococcus anophagefferens – a species notorious for causing harmful algal blooms. To investigate genome evolution across Pelagophyceae and within Aureococcus anophagefferens , we used long-read sequencing to produce high-quality genome assemblies for five strains of Ac. anophagefferens (52-54 megabase-pairs; Mbp), a telomere-to-telomere assembly for Pelagomonas calceolata (32 Mbp), and the first reference genome for Aureoumbra lagunensis (41 Mbp). Using comparative genomics and phylogenetics, we show remarkable strain level genetic variation in Ac. anophagefferens with a pangenome (23,356 orthogroups) that is 81.1% core and 18.9% accessory. Although gene content variation within Ac. anophagefferens does not appear to be largely driven by recent prokaryotic LGTs (2.6% of accessory orthogroups), 368 orthogroups were acquired from bacteria in a common ancestor of all analyzed strains and are not found in P. calceolata or Au. lagunensis . 1,077 recent LGTs from prokaryotes and viruses were identified within Pelagophyceae overall, constituting 3.5-4.0% of the orthogroups in each species. This includes genes likely contributing to the ecological success of pelagophytes globally and in long-lasting harmful blooms.
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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 | 0.001 |
| 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".