Distribution and Phylogeny of EFL and EF-1α in Euglenozoa Suggest Ancestral Co-Occurrence Followed by Differential Loss
Bibliographic record
Abstract
BACKGROUND: The eukaryotic elongation factor EF-1alpha (also known as EF1A) catalyzes aminoacyl-tRNA binding by the ribosome during translation. Homologs of this essential protein occur in all domains of life, and it was previously thought to be ubiquitous in eukaryotes. Recently, however, a number of eukaryotes were found to lack EF-1alpha and instead encode a related protein called EFL (for EF-Like). EFL-encoding organisms are scattered widely across the tree of eukaryotes, and all have close relatives that encode EF-1alpha. This intriguingly complex distribution has been attributed to multiple lateral transfers because EFL's near mutual exclusivity with EF-1alpha makes an extended period of co-occurrence seem unlikely. However, differential loss may play a role in EFL evolution, and this possibility has been less widely discussed. METHODOLOGY/PRINCIPAL FINDINGS: We have undertaken an EST- and PCR-based survey to determine the distribution of these two proteins in a previously under-sampled group, the Euglenozoa. EF-1alpha was found to be widespread and monophyletic, suggesting it is ancestral in this group. EFL was found in some species belonging to each of the three euglenozoan lineages, diplonemids, kinetoplastids, and euglenids. CONCLUSIONS/SIGNIFICANCE: Interestingly, the kinetoplastid EFL sequences are specifically related despite the fact that the lineages in which they are found are not sisters to one another, suggesting that EFL and EF-1alpha co-occurred in an early ancestor of kinetoplastids. This represents the strongest phylogenetic evidence to date that differential loss has contributed to the complex distribution of EFL and EF-1alpha.
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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 | 0.001 | 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".