Abiotic Stress Phenotypes Are Associated with Conserved Genes Derived from Transposable Elements
Bibliographic record
Abstract
Plant phenomics offers unique opportunities to accelerate our understanding of gene function and plant response to different environments, and may be particularly useful for studying previously uncharacterized genes. One important type of poorly characterized genes is those derived from transposable elements (TEs), which have departed from a mobility-driven lifestyle to attain new adaptive roles for the host (exapted TEs). We used phenomics approaches, coupled with reverse genetics, to analyze T-DNA insertion mutants of both previously reported and novel protein-coding exapted TEs in the model plant Arabidopsis thaliana. We show that mutations in most of these exapted TEs result in phenotypes, particularly when challenged by abiotic stress. We built statistical multi-dimensional phenotypic profiles and compared them to wild-type and known stress responsive mutant lines for each particular stress condition. We found that these exapted TEs may play roles in responses to phosphate limitation, tolerance to high salt concentration, freezing temperatures, and arsenic toxicity. These results not only experimentally validate a large set of putative functional exapted TEs recently discovered through computational analysis, but also uncover additional novel phenotypes for previously well-characterized exapted TEs in A. thaliana.
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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 | 0.001 |
| 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".