Development and Application of Novel Constructs to Score C:G-to-T:A Transitions and Homologous Recombination in Arabidopsis
Bibliographic record
Abstract
We report on the development of five missense mutants and one recombination substrate of the beta-glucuronidase (GUS)-encoding gene of Escherichia coli and their use for detecting mutation and recombination events in transgenic Arabidopsis (Arabidopsis thaliana) plants by reactivation of GUS activity in clonal sectors. The missense mutants were designed to find C:G-to-T:A transitions in a symmetrical sequence context and are in that respect complementary to previously published GUS point mutants. Small peptide tags (hemagglutinin tag and Strep tag II) and green fluorescent protein were translationally fused to GUS, which offers possibilities to check for mutant GUS production levels. We show that spontaneous mutation and recombination events took place. Mutagenic treatment of the plants with ethyl methanesulfonate and ultraviolet-C increased the number of mutations, validating the use of these constructs to measure mutation and recombination frequencies in plants exposed to biotic or abiotic stress conditions, or in response to different genetic backgrounds. Plants were also subjected to heavy metals, methyl jasmonate, salicylic acid, and heat stress, for which no effect could be seen. Together with an ethyl methanesulfonate mutation induction level much higher than previously described, the need is illustrated for many available scoring systems in parallel. Because all GUS missense mutants were cloned in a bacterial expression vector, they can also be used to score mutation events in E. coli.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".