Deletions within intronic <scp>T‐DNA</scp> lead to reversion of <scp>T‐DNA</scp> mutant phenotypes
Bibliographic record
Abstract
Agrobacterium-mediated transformation enables random transfer-DNA (T-DNA) insertion into plant genomes. T-DNA insertion into a gene's exons, introns or untranscribed regions close to the start or stop codon can disrupt gene function. Such T-DNA mutants have been useful for reverse genetics analysis, especially in Arabidopsis thaliana. As T-DNAs are inserted into genomic DNA, they are generally believed to be stably inherited. Here, we report a phenomenon of reversion of intronic T-DNA mutant phenotypes. From a suppressor screen using intronic T-DNA pi4kβ1,2 double mutant, we recovered intragenic mutants of pi4kβ1, which suppressed the autoimmunity of the double mutant. These mutants carried deletions in the intronic T-DNAs, resulting in elevated transcription of normal PI4Kβ1. Such reversion of T-DNA insertional mutant phenotype stresses the need for caution when using intronic T-DNA mutants and reiterates the importance of using irreversible null mutant alleles in genetic analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".