Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida, Haptophyta and Cryptista
Bibliographic record
Abstract
Assembling the global eukaryotic tree of life has long been a major effort of Biology. In recent years, pushed by the new availability of genome-scale data for microbial eukaryotes, it has become possible to revisit many evolutionary enigmas. However, some of the most ancient nodes, which are essential for inferring a stable tree, have remained highly controversial. Among other reasons, the lack of adequate genomic datasets for key taxa has prevented the robust reconstruction of early diversification events. In this context, the centrohelid heliozoans are particularly relevant for reconstructing the tree of eukaryotes because they represent one of the last substantial groups that was missing large and diverse genomic data. Here, we filled this gap by sequencing high-quality transcriptomes for four centrohelid lineages, each corresponding to a different family. Combining these new data with a broad eukaryotic sampling, we produced a gene-rich taxon-rich phylogenomic dataset that enabled us to refine the structure of the tree. Specifically, we show that (i) centrohelids relate to haptophytes, confirming Haptista; (ii) Haptista relates to SAR; (iii) Cryptista share strong affinity with Archaeplastida; and (iv) Haptista + SAR is sister to Cryptista + Archaeplastida. The implications of this topology are discussed in the broader context of plastid evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".